Machine Learning Research Topics for MS PhD
Machine learning research topic ideas for ms, or ph.d. degree.
I am sharing with you some of the research topics regarding Machine Learning that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.
- Applications of machine learning to machine fault diagnosis: A review and roadmap
- Significant applications of machine learning for COVID-19 pandemic
- Quantum chemistry in the age of machine learning
- A survey on machine learning for data fusion
- Artificial intelligence and machine learning to fight COVID-19
- Machine learning for molecular simulation
- A survey on distributed machine learning
- Explainable machine learning for scientific insights and discoveries
- When Machine Learning Meets Privacy: A Survey and Outlook
- Machine learning testing: Survey, landscapes and horizons
- Machine learning and psychological research: The unexplored effect of measurement
- Universal differential equations for scientific machine learning
- Machine learning for active matter
- Exploring chemical compound space with quantum-based machine learning
- Ten challenges in advancing machine learning technologies toward 6G
- Machine learning for materials scientists: An introductory guide toward best practices
- Lessons from archives: Strategies for collecting sociocultural data in machine learning
- Tslearn, a machine learning toolkit for time series data
- A snapshot of the frontiers of fairness in machine learning
- How machine learning will transform biomedicine
- An introduction to machine learning
- Machine learning for protein folding and dynamics
- DScribe: Library of descriptors for machine learning in materials science
- Advances of four machine learning methods for spatial data handling: A review
- New machine learning method for image-based diagnosis of COVID-19
- Applications of machine learning methods for engineering risk assessment–A review
- A critical review of machine learning of energy materials
- State-of-the-art on research and applications of machine learning in the building life cycle
- Elastic machine learning algorithms in amazon sagemaker
- Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
- Assessment of supervised machine learning methods for fluid flows
- Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
- First-order and Stochastic Optimization Methods for Machine Learning
- Explainable machine learning in deployment
- Machine learning for enterprises: Applications, algorithm selection, and challenges
- Multiscale modeling meets machine learning: What can we learn?
- Machine learning from a continuous viewpoint, I
- Machine learning applications in systems metabolic engineering
- Single trajectory characterization via machine learning
- Adversarial machine learning-industry perspectives
- Machine learning approaches for thermoelectric materials research
- Machine learning approaches for analyzing and enhancing molecular dynamics simulations
- Open graph benchmark: Datasets for machine learning on graphs
- Preparing medical imaging data for machine learning
- On hyperparameter optimization of machine learning algorithms: Theory and practice
- Machine learning techniques for the diagnosis of Alzheimer’s disease: A review
- CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design
- Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
- Personality research and assessment in the era of machine learning
- Machine learning force fields
- Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
- Applications of artificial intelligence and machine learning in smart cities
- Machine learning and wearable devices of the future
- Integrating physics-based modeling with machine learning: A survey
- The non-iid data quagmire of decentralized machine learning
- Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
- Machine learning and soil sciences: A review aided by machine learning tools
- Machine learning and deep learning techniques for cybersecurity: a review
- Identifying ethical considerations for machine learning healthcare applications
- Introduction to machine learning
- Machine learning for quantum matter
- Machine learning for glass science and engineering: A review
- Machine learning for continuous innovation in battery technologies
- Applying machine learning in science assessment: a systematic review
- Machine learning for interatomic potential models
- Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
- FCHL revisited: Faster and more accurate quantum machine learning
- Machine-learning-assisted synthesis of polar racemates
- Clinical text data in machine learning: Systematic review
- Machine learning for genetic prediction of psychiatric disorders: a systematic review
- Wake modeling of wind turbines using machine learning
- A survey of surveys on the use of visualization for interpreting machine learning models
- Big-data science in porous materials: materials genomics and machine learning
- Machine learning
- The rise of machine learning for detection and classification of malware: Research developments, trends and challenges
- Building thermal load prediction through shallow machine learning and deep learning
- Machine learning technology in biodiesel research: A review
- Machine learning driven smart electric power systems: Current trends and new perspectives
- What role does hydrological science play in the age of machine learning?
- Early diagnosis of hepatocellular carcinoma using machine learning method
- Image-based cardiac diagnosis with machine learning: a review
- Unsupervised machine learning and band topology
- Cybersecurity data science: an overview from machine learning perspective
- A survey of visual analytics techniques for machine learning
- Quantum embeddings for machine learning
- M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
- Coronavirus Disease (COVID-19): A Machine learning bibliometric analysis
- Special issue on machine learning and data-driven methods in fluid dynamics
- A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic
- Metallurgy, mechanistic models and machine learning in metal printing
- A perspective on using machine learning in 3D bioprinting
- COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach
- The relationship between trust in AI and trustworthy machine learning technologies
- Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program)
- COVID-19 future forecasting using supervised machine learning models
- Mapping landslides on EO data: Performance of deep learning models vs. traditional machine learning models
- A biochemically-interpretable machine learning classifier for microbial GWAS
- Identifying scenarios of benefit or harm from kidney transplantation during the COVID‐19 pandemic: a stochastic simulation and machine learning study
- Machine learning analysis of whole mouse brain vasculature
- Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
- Machine Learning Calabi–Yau Metrics
- Opening the black box: Interpretable machine learning for geneticists
- Machine learning in additive manufacturing: State-of-the-art and perspectives
- Machine learning approach to identify stroke within 4.5 hours
- Machine-learning quantum states in the NISQ era
- Machine learning as an early warning system to predict financial crisis
- Interpretable machine learning
- Landslide identification using machine learning
- Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
- Recent advances on constraint-based models by integrating machine learning
- Machine Learning in oncology: A clinical appraisal
- Polymer design using genetic algorithm and machine learning
- Performance evaluation of machine learning methods for forest fire modeling and prediction
- Machine learning approach for confirmation of covid-19 cases: Positive, negative, death and release
- Learning earth system models from observations: machine learning or data assimilation?
- Machine Learning Meets Quantum Physics
- Clinical applications of continual learning machine learning
- Machine learning: accelerating materials development for energy storage and conversion
- Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
- A review on machine learning forecasting growth trends and their real-time applications in different energy systems
- A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
- Machine learning in geo-and environmental sciences: From small to large scale
- Blockchain and machine learning for communications and networking systems
- Machine learning and natural language processing in psychotherapy research: Alliance as example use case.
- Machine Learning for Solar Array Monitoring, Optimization, and Control
- Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
- Machine learning in agricultural and applied economics
- AutoML-zero: evolving machine learning algorithms from scratch
- A comprehensive survey of loss functions in machine learning
- COVID-19 epidemic analysis using machine learning and deep learning algorithms
- Attention in psychology, neuroscience, and machine learning
- Get rich or die trying… finding revenue model fit using machine learning and multiple cases
- How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection
- Machine learning based solutions for security of Internet of Things (IoT): A survey
- Introduction to machine learning, neural networks, and deep learning
- Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0
- Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies
- Determinants of base editing outcomes from target library analysis and machine learning
- A primer for understanding radiology articles about machine learning and deep learning
- A machine‐learning approach for earthquake magnitude estimation
- Applying machine learning in liver disease and transplantation: a comprehensive review
- Machine learning approaches for elucidating the biological effects of natural products
- Systematic review of machine learning for diagnosis and prognosis in dermatology
- Early prediction of circulatory failure in the intensive care unit using machine learning
- Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions
- Machine learning applications for mass spectrometry-based metabolomics
- Improving the accuracy of medical diagnosis with causal machine learning
- A machine learning forecasting model for COVID-19 pandemic in India
- Machine learning in psychometrics and psychological research
- Automatic detection of coronavirus disease (covid-19) in x-ray and ct images: A machine learning-based approach
- Machine learning predicts new anti-CRISPR proteins
- Machine learning approaches to drug response prediction: challenges and recent progress
- Machine learning prediction of mechanical properties of concrete: Critical review
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
- Crop yield prediction using machine learning: A systematic literature review
- Julia language in machine learning: Algorithms, applications, and open issues
- The impact of machine learning on patient care: A systematic review
- A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys
- Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
- Applications of machine learning predictive models in the chronic disease diagnosis
- Your evidence? Machine learning algorithms for medical diagnosis and prediction
- Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
- Towards the systematic reporting of the energy and carbon footprints of machine learning
- Machine learning accurate exchange and correlation functionals of the electronic density
- Machine learning in additive manufacturing: A review
- Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches
- Explaining machine learning classifiers through diverse counterfactual explanations
- A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models
- A review of epileptic seizure detection using machine learning classifiers
- Ai explainability 360: An extensible toolkit for understanding data and machine learning models
- Using machine learning to predict decisions of the European Court of Human Rights
- Intelligent edge computing based on machine learning for smart city
- Machine learning and its applications in plant molecular studies
- Machine learning for fluid mechanics
- A universal machine learning algorithm for large-scale screening of materials
- Coronavirus (covid-19) classification using ct images by machine learning methods
- Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential
- A survey of online data-driven proactive 5g network optimisation using machine learning
- Machine learning algorithms for construction projects delay risk prediction
- Toward interpretable machine learning: Transparent deep neural networks and beyond
- Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry
- PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms
- Machine learning-based classification of vector vortex beams
- Machine‐learning scoring functions for structure‐based drug lead optimization
- Potential neutralizing antibodies discovered for novel corona virus using machine learning
- Machine learning and artificial intelligence in haematology
- Machine learning on graphs: A model and comprehensive taxonomy
- Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning
- MadMiner: Machine learning-based inference for particle physics
- Machine learning for asset managers
- Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics
- Statistical and machine-learning methods for clearance time prediction of road incidents: A methodology review
- Hierarchical machine learning of potential energy surfaces
- Hybrid decision tree-based machine learning models for short-term water quality prediction
- Machine-learning studies on spin models
- Machine learning and data analytics for the IoT
- Quantum adversarial machine learning
- Engaging proactive control: Influences of diverse language experiences using insights from machine learning.
- Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
- Corporate default forecasting with machine learning
- Identification of light sources using machine learning
- Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
- Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making
- On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning
- Recent developments in machine learning for energy systems reliability management
- Machine learning and AI in marketing–Connecting computing power to human insights
- Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in …
- Machine-learning-accelerated perovskite crystallization
- A review on machine learning in 3D printing: Applications, potential, and challenges
- Integrated machine learning methods with resampling algorithms for flood susceptibility prediction
- Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
- An open source machine learning framework for efficient and transparent systematic reviews
- Machine learning in breast MRI
- Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques
- Machine learning models for secure data analytics: A taxonomy and threat model
- Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities
- Prediction of droughts over Pakistan using machine learning algorithms
- From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
- Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI
- Physics-informed machine learning: case studies for weather and climate modelling
- How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?
- Selecting appropriate machine learning methods for digital soil mapping
- Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
- A clinician’s guide to artificial intelligence: how to critically appraise machine learning studies
- Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability
- Machine learning and artificial intelligence
- COVID-19 diagnosis prediction in emergency care patients: a machine learning approach
- The use of machine learning techniques in trauma-related disorders: a systematic review
- A review of machine learning applications in wildfire science and management
- Land-use land-cover classification by machine learning classifiers for satellite observations—A review
- Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
- Machine learning for suicidology: A practical review of exploratory and hypothesis-driven approaches
- Fairness in machine learning
- A machine learning-based model for survival prediction in patients with severe COVID-19 infection
- Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning
- Modelling of shallow landslides with machine learning algorithms
- Predicting Regioselectivity in Radical C− H Functionalization of Heterocycles through Machine Learning
- Understanding from machine learning models
- Nothing to disconnect from? Being singular plural in an age of machine learning
- Supervised classification algorithms in machine learning: A survey and review
- Can machine learning find extraordinary materials?
- Incorporating biological structure into machine learning models in biomedicine
- Bitcoin price prediction using machine learning: An approach to sample dimension engineering
- Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0
- Secure, privacy-preserving and federated machine learning in medical imaging
- Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
- Classifying earthquake damage to buildings using machine learning
- The state of the art in enhancing trust in machine learning models with the use of visualizations
- The digital divide in light of sustainable development: An approach through advanced machine learning techniques
- Machine learning-based prediction of COVID-19 diagnosis based on symptoms
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
- Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry
- Predicting standardized streamflow index for hydrological drought using machine learning models
- Machine learning line bundle cohomology
- Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
- Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study
- Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous …
- Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
- Secure and robust machine learning for healthcare: A survey
- A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions
- Dynamic backdoor attacks against machine learning models
- Flood susceptibility modelling using advanced ensemble machine learning models
- Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
- Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach
- Machine learning in rheumatology approaches the clinic
- A novel randomized machine learning approach: Reservoir computing extreme learning machine
- 5G vehicular network resource management for improving radio access through machine learning
- Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems
- Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning
- Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
- Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm
- Data-driven symbol detection via model-based machine learning
- Machine learning models for drug–target interactions: current knowledge and future directions
- Machine‐learning scoring functions for structure‐based virtual screening
- Machine learning and artificial intelligence: Definitions, applications, and future directions
- Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review
- Closed-loop optimization of fast-charging protocols for batteries with machine learning
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
- Improving risk prediction in heart failure using machine learning
- A perspective on machine learning methods in turbulence modeling
- Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
- Machine learning in fetal cardiology: What to expect
- Molecular machine learning: the future of synthetic chemistry?
- Application and comparison of several machine learning algorithms and their integration models in regression problems
- A machine learning application for raising wash awareness in the times of covid-19 pandemic
- Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media
- Thirty years of machine learning: The road to Pareto-optimal wireless networks
- A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
- Predicting and explaining corruption across countries: A machine learning approach
- Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning
- Machine learning: hands-on for developers and technical professionals
- Conservation machine learning: a case study of random forests
- Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ …
- BLAZE: blazing fast privacy-preserving machine learning
- Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches
- Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms
- Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors
- A review on machine learning algorithms to predict daylighting inside buildings
- Using machine learning in psychiatry: the need to establish a framework that nurtures trustworthiness
- Machine learning threatens 5G security
- FUNDAMENTAL APPLICATIONS OF MACHINE LEARNING ACROSS THE GLOBE
- Machine learning decomposition onset temperature of lubricant additives
- An efficient optimization approach for designing machine learning models based on genetic algorithm
- Deep machine learning approach to develop a new asphalt pavement condition index
- Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results
- Review of machine learning methods in soft robotics
- Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning
- Utilization of machine-learning models to accurately predict the risk for critical COVID-19
- A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
- Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II
- Reflections on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning
- Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
- Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models
- Prediction of CO2 solubility in ionic liquids using machine learning methods
- Forecasting air quality in Taiwan by using machine learning
- Machine learning and network analyses reveal disease subtypes of pancreatic cancer and their molecular characteristics
- A trustworthy privacy preserving framework for machine learning in industrial iot systems
- Structural Deformation Controls Charge Losses in MAPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics
- Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models
- A path for translation of machine learning products into healthcare delivery
- Data and its (dis) contents: A survey of dataset development and use in machine learning research
- Gas turbine performance prediction via machine learning
- Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission
- Biophysical prediction of protein–peptide interactions and signaling networks using machine learning
- Identification of high impact factors of air quality on a national scale using big data and machine learning techniques
- Effect of data encoding on the expressive power of variational quantum-machine-learning models
- Deep Dive into Machine Learning Models for Protein Engineering
- Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
- Machine learning glass transition temperature of polymers
- Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison
- Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox
- Testing machine learning based systems: a systematic mapping
- Flexible piezoelectric acoustic sensors and machine learning for speech processing
- Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
- Analyzing and predicting students’ performance by means of machine learning: a review
- GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning.
- The role of sensors, big data and machine learning in modern animal farming
- Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets
- Machine learning for combinatorial optimization: a methodological tour d’horizon
- Machine learning: Algorithms, real-world applications and research directions
- Machine-learning approach expands the repertoire of anti-CRISPR protein families
- ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data
- Machine Learning Gauged Supergravity
- Machine learning to reveal nanoparticle dynamics from liquid-phase tem videos
- Suicidal ideation detection: A review of machine learning methods and applications
- Multitask learning over graphs: An approach for distributed, streaming machine learning
- 6G white paper on machine learning in wireless communication networks
- Machine learning prediction of critical transition and system collapse
- Machine learning Calabi-Yau four-folds
- Machine learning and big data in the impact literature. A bibliometric review with scientific mapping in Web of science
- A Machine Learning‐Based Global Atmospheric Forecast Model
- MP2ML: a mixed-protocol machine learning framework for private inference
- Machine Learning in Quantitative PET Imaging
- Visual analysis of discrimination in machine learning
- Machine learning for the solution of the Schrödinger equation
- Real-world application of machine-learning-based fault detection trained with experimental data
- Machine learning strategies applied to the control of a fluidic pinball
- Machine-learning-optimized aperiodic superlattice minimizes coherent phonon heat conduction
- Combining data assimilation and machine learning to infer unresolved scale parametrization
- Introducing Machine Learning: Science and Technology
- Machine Learning for Structural Materials
- Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
- Estimating entropy production by machine learning of short-time fluctuating currents
- Intelligent traffic control for autonomous vehicle systems based on machine learning
- Diabetes prediction using ensembling of different machine learning classifiers
- Functional input and membership characteristics in the accuracy of machine learning approach for estimation of multiphase flow
- Decentralized stochastic optimization and machine learning: A unified variance-reduction framework for robust performance and fast convergence
- Machine learning for detecting early infarction in acute stroke with non–contrast-enhanced CT
- Machine learning the thermodynamic arrow of time
- Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning
- Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
- Survey on IoT security: challenges and solution using machine learning, artificial intelligence and blockchain technology
- A segmented machine learning modeling approach of social media for predicting occupancy
- Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city
- Double debiased machine learning nonparametric inference with continuous treatments
- Long-term prediction of chaotic systems with machine learning
- SurvLIME: A method for explaining machine learning survival models
- Teaching optics to a machine learning network
- Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
- toward enhanced State of charge estimation of Lithium-ion Batteries Using optimized Machine Learning techniques
- When do short-range atomistic machine-learning models fall short?
- Machine learning of noise-resilient quantum circuits
- Machine learning based code dissemination by selection of reliability mobile vehicles in 5G networks
- Predicting product return volume using machine learning methods
- A non-review of Quantum Machine Learning: trends and explorations
- A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications
- Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms
- Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System
- Machine learning-based design strategy for 3D printable bioink: elastic modulus and yield stress determine printability
- The impact of entrepreneurship orientation on project performance: A machine learning approach
- Data science in economics: comprehensive review of advanced machine learning and deep learning methods
- Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in …
- Machine learning for dummies
- Prediction of condition-specific regulatory genes using machine learning
- Machine learning the magnetocaloric effect in manganites from lattice parameters
- giotto-tda: A topological data analysis toolkit for machine learning and data exploration
- Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia
- Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes
- Analysis on novel coronavirus (COVID-19) using machine learning methods
- Towards novel insights in lattice field theory with explainable machine learning
- Infrared spectroscopy data-and physics-driven machine learning for characterizing surface microstructure of complex materials
- Machine learning models to quantify and map daily global solar radiation and photovoltaic power
- Machine learning based intrusion detection systems for IoT applications
- Predicting bank insolvencies using machine learning techniques
- Precision psychiatry applications with pharmacogenomics: Artificial intelligence and machine learning approaches
- Automated classification of significant prostate cancer on MRI: A systematic review on the performance of machine learning applications
- Correlation between temperature and COVID-19 (suspected, confirmed and death) cases based on machine learning analysis
- Machine learning in IoT security: Current solutions and future challenges
- The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review
- Machine learning and big scientific data
- Machine learning for model error inference and correction
- Participation is not a design fix for machine learning
- Considerations for selecting a machine learning technique for predicting deforestation
- Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
- Advancing Biosensors with Machine Learning
- The need for a system view to regulate artificial intelligence/machine learning-based software as medical device
- Statistical and machine learning models in credit scoring: A systematic literature survey
- A review on cooling performance enhancement for phase change materials integrated systems—flexible design and smart control with machine learning applications
- Linear Algebra and Optimization for Machine Learning
- Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms
- Submodularity in action: From machine learning to signal processing applications
- Introduction of a time series machine learning methodology for the application in a production system
- Machine learning the central magnetic flux density of superconducting solenoids
- Flexible machine learning-based cyberattack detection using spatiotemporal patterns for distribution systems
- Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science
- A machine learning model for emotion recognition from physiological signals
- Toward an intelligent edge: Wireless communication meets machine learning
- Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction
- Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey
- Empirical asset pricing via machine learning
- Comparison of machine learning models for gully erosion susceptibility mapping
- Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings
- Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions
- Machine learning and knowledge graph based design rule construction for additive manufacturing
- Machine learning for long-distance quantum communication
- Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed, Iran
- Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays
- Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease
- Machine learning dihydrogen activation in the chemical space surrounding Vaska’s complex
- On classifying sepsis heterogeneity in the ICU: insight using machine learning
- Phishing web site detection using diverse machine learning algorithms
- Automated self-optimisation of multi-step reaction and separation processes using machine learning
- Evaluation of short-term freeway speed prediction based on periodic analysis using statistical models and machine learning models
- Explainable artificial intelligence and machine learning: A reality rooted perspective
- Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
- Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process
- Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models
- Inverse design of topological metaplates for flexural waves with machine learning
- Assessing the drivers of machine learning business value
- Neural additive models: Interpretable machine learning with neural nets
- Conventional and machine learning approaches as countermeasures against hardware trojan attacks
- Using machine learning to estimate unobserved COVID-19 infections in North America
- Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big …
- Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
- Improving cyberbullying detection using Twitter users’ psychological features and machine learning
- Classifying convective storms using machine learning
- Robustness in machine learning explanations: does it matter?
- Machine learning modeling of lattice constants for half-Heusler alloys
- Asthma-prone areas modeling using a machine learning model
- Identification of risk factors associated with obesity and overweight—A machine learning overview
- Fairness in Machine Learning for Healthcare
- A review of machine learning approaches to power system security and stability
- Artificial intelligence and machine learning in design of mechanical materials
- Learning to Make chemical predictions: the Interplay of feature representation, data, and machine learning methods
- Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data
- High-performance diagnosis of sleep disorders: a novel, accurate and fast machine learning approach using electroencephalographic data
- Quantum machine learning models are kernel methods
- Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
- Machine learning lattice constants for cubic perovskite A 2 2+ BB′ O 6 compounds
- Adversarial Machine Learning for 5G Communications Security
- Machine learning lattice parameters of monoclinic double perovskites
- A comprehensive survey and analysis of generative models in machine learning
- Experimental velocity data estimation for imperfect particle images using machine learning
- Artificial intelligence and machine learning in computational nanotoxicology: Unlocking and empowering nanomedicine
- Carbon trading volume and price forecasting in China using multiple machine learning models
- Machine learning for structural health monitoring: challenges and opportunities
- Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems
- Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks
- Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms
- Machine learning on density and elastic property of oxide glasses driven by large dataset
- Scientific intuition inspired by machine learning generated hypotheses
- A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques
- Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
- Enhancing gravitational-wave science with machine learning
- Machine learning active-nematic hydrodynamics
- Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach
- Electronic skins and machine learning for intelligent soft robots
- Artificial intelligence and machine learning in respiratory medicine
- Machine Learning Band Gaps of Doped-TiO2 Photocatalysts from Structural and Morphological Parameters
- Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
- Machine learning for intelligent optical networks: A comprehensive survey
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10 Compelling Machine Learning Ph.D. Dissertations for 2020
Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 19, 2020 Daniel Gutierrez, ODSC
As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour arXiv.org for late-breaking papers that show trends and reveal fertile areas of research. Other sources of valuable research developments are in the form of Ph.D. dissertations, the culmination of a doctoral candidate’s work to confer his/her degree. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. Their dissertations are highly focused on a specific problem. If you can find a dissertation that aligns with your areas of interest, consuming the research is an excellent way to do a deep dive into the technology. After reviewing hundreds of recent theses from universities all over the country, I present 10 machine learning dissertations that I found compelling in terms of my own areas of interest.
[Related article: Introduction to Bayesian Deep Learning ]
I hope you’ll find several that match your own fields of inquiry. Each thesis may take a while to consume but will result in hours of satisfying summer reading. Enjoy!
1. Bayesian Modeling and Variable Selection for Complex Data
As we routinely encounter high-throughput data sets in complex biological and environmental research, developing novel models and methods for variable selection has received widespread attention. This dissertation addresses a few key challenges in Bayesian modeling and variable selection for high-dimensional data with complex spatial structures.
2. Topics in Statistical Learning with a Focus on Large Scale Data
Big data vary in shape and call for different approaches. One type of big data is the tall data, i.e., a very large number of samples but not too many features. This dissertation describes a general communication-efficient algorithm for distributed statistical learning on this type of big data. The algorithm distributes the samples uniformly to multiple machines, and uses a common reference data to improve the performance of local estimates. The algorithm enables potentially much faster analysis, at a small cost to statistical performance.
Another type of big data is the wide data, i.e., too many features but a limited number of samples. It is also called high-dimensional data, to which many classical statistical methods are not applicable.
This dissertation discusses a method of dimensionality reduction for high-dimensional classification. The method partitions features into independent communities and splits the original classification problem into separate smaller ones. It enables parallel computing and produces more interpretable results.
3. Sets as Measures: Optimization and Machine Learning
The purpose of this machine learning dissertation is to address the following simple question:
How do we design efficient algorithms to solve optimization or machine learning problems where the decision variable (or target label) is a set of unknown cardinality?
Optimization and machine learning have proved remarkably successful in applications requiring the choice of single vectors. Some tasks, in particular many inverse problems, call for the design, or estimation, of sets of objects. When the size of these sets is a priori unknown, directly applying optimization or machine learning techniques designed for single vectors appears difficult. The work in this dissertation shows that a very old idea for transforming sets into elements of a vector space (namely, a space of measures), a common trick in theoretical analysis, generates effective practical algorithms.
4. A Geometric Perspective on Some Topics in Statistical Learning
Modern science and engineering often generate data sets with a large sample size and a comparably large dimension which puts classic asymptotic theory into question in many ways. Therefore, the main focus of this dissertation is to develop a fundamental understanding of statistical procedures for estimation and hypothesis testing from a non-asymptotic point of view, where both the sample size and problem dimension grow hand in hand. A range of different problems are explored in this thesis, including work on the geometry of hypothesis testing, adaptivity to local structure in estimation, effective methods for shape-constrained problems, and early stopping with boosting algorithms. The treatment of these different problems shares the common theme of emphasizing the underlying geometric structure.
5. Essays on Random Forest Ensembles
A random forest is a popular machine learning ensemble method that has proven successful in solving a wide range of classification problems. While other successful classifiers, such as boosting algorithms or neural networks, admit natural interpretations as maximum likelihood, a suitable statistical interpretation is much more elusive for a random forest. The first part of this dissertation demonstrates that a random forest is a fruitful framework in which to study AdaBoost and deep neural networks. The work explores the concept and utility of interpolation, the ability of a classifier to perfectly fit its training data. The second part of this dissertation places a random forest on more sound statistical footing by framing it as kernel regression with the proximity kernel. The work then analyzes the parameters that control the bandwidth of this kernel and discuss useful generalizations.
6. Marginally Interpretable Generalized Linear Mixed Models
A popular approach for relating correlated measurements of a non-Gaussian response variable to a set of predictors is to introduce latent random variables and fit a generalized linear mixed model. The conventional strategy for specifying such a model leads to parameter estimates that must be interpreted conditional on the latent variables. In many cases, interest lies not in these conditional parameters, but rather in marginal parameters that summarize the average effect of the predictors across the entire population. Due to the structure of the generalized linear mixed model, the average effect across all individuals in a population is generally not the same as the effect for an average individual. Further complicating matters, obtaining marginal summaries from a generalized linear mixed model often requires evaluation of an analytically intractable integral or use of an approximation. Another popular approach in this setting is to fit a marginal model using generalized estimating equations. This strategy is effective for estimating marginal parameters, but leaves one without a formal model for the data with which to assess quality of fit or make predictions for future observations. Thus, there exists a need for a better approach.
This dissertation defines a class of marginally interpretable generalized linear mixed models that leads to parameter estimates with a marginal interpretation while maintaining the desirable statistical properties of a conditionally specified model. The distinguishing feature of these models is an additive adjustment that accounts for the curvature of the link function and thereby preserves a specific form for the marginal mean after integrating out the latent random variables.
7. On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media
The objective of this dissertation is to explore the use of machine learning algorithms in understanding and detecting hate speech, hate speakers and polarized groups in online social media. Beginning with a unique typology for detecting abusive language, the work outlines the distinctions and similarities of different abusive language subtasks (offensive language, hate speech, cyberbullying and trolling) and how we might benefit from the progress made in each area. Specifically, the work suggests that each subtask can be categorized based on whether or not the abusive language being studied 1) is directed at a specific individual, or targets a generalized “Other” and 2) the extent to which the language is explicit versus implicit. The work then uses knowledge gained from this typology to tackle the “problem of offensive language” in hate speech detection.
8. Lasso Guarantees for Dependent Data
Serially correlated high dimensional data are prevalent in the big data era. In order to predict and learn the complex relationship among the multiple time series, high dimensional modeling has gained importance in various fields such as control theory, statistics, economics, finance, genetics and neuroscience. This dissertation studies a number of high dimensional statistical problems involving different classes of mixing processes.
9. Random forest robustness, variable importance, and tree aggregation
Random forest methodology is a nonparametric, machine learning approach capable of strong performance in regression and classification problems involving complex data sets. In addition to making predictions, random forests can be used to assess the relative importance of feature variables. This dissertation explores three topics related to random forests: tree aggregation, variable importance, and robustness.
10. Climate Data Computing: Optimal Interpolation, Averaging, Visualization and Delivery
This dissertation solves two important problems in the modern analysis of big climate data. The first is the efficient visualization and fast delivery of big climate data, and the second is a computationally extensive principal component analysis (PCA) using spherical harmonics on the Earth’s surface. The second problem creates a way to supply the data for the technology developed in the first. These two problems are computationally difficult, such as the representation of higher order spherical harmonics Y400, which is critical for upscaling weather data to almost infinitely fine spatial resolution.
I hope you enjoyed learning about these compelling machine learning dissertations.
Editor’s note: Interested in more data science research? Check out the Research Frontiers track at ODSC Europe this September 17-19 or the ODSC West Research Frontiers track this October 27-30.
Daniel Gutierrez, ODSC
Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.
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Machine Learning - CMU
Phd program in machine learning.
Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.
Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.
The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning. For questions and concerns, please contact us .
Financial Support It will be our responsibility, not yours, to worry about your tuition and stipend while you are in our program. We are committed to providing your full tuition and stipend support for the coming academic year. We intend to continue this support as long as you continue to make satisfactory progress in our program. Students who do not have external financial support will be funded via graduate assistantships, awarded for a nine-month period from September to May, which would normally come from their advisor’s grants. Because of that, specific research opportunities may be constrained by funding availability. Students who have partial external financial support will be supplemented to bring them to the full level of support, and often receive an additional supplement on top of that. If you have dependents, we will also pay you a dependency allowance that is 10% of the MLD monthly base stipend per eligible dependent, unless you have a spouse or qualifying domestic partner who earns more than $500 per month.
PhD Program Requirements
Application Information For Fall 2022 applications, GRE Scores are optional but strongly recommended. If you cannot submit GRE scores, explain the reason in your Statement of Purpose. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.
If you are an international applicant and your native language (language spoken from birth) is not English, an official copy of an English proficiency score report is required. The English proficiency requirement cannot be waived for any reason. We strongly encourage applicants to take either the TOEFL or IELTS. In cases where these are not available it is acceptable to take the Duolingo test. We discourage the use of the "TOEFL ITP Plus for China", since speaking is not scored. Additional details are provided on the Frequently Asked Questions page.
We recommend a combined TOEFL score of 100, with no subscore below 25, although we will make exceptions to this cutoff in exceptional cases. Unofficially, we recommend a high level of comfort with math (particularly linear algebra, probability, and proofs) and computer programming (at the level of an undergraduate degree in computer science, although many of our applicants get the necessary experience without majoring in CS). It is possible to fill in some of this background on the fly, but you will be working hard to do so! In addition, the program is very competitive, so successful applications always stand out in some way from their peers -- for example grades, research experience, or recommendation letters.
Graduate Online Application
- Early Application Deadline – November 29, 2022 (3:00 p.m. EST)
- Final Application Deadline - December 12, 2022 (3:00 p.m. EST)
M.Tech/Ph.D Thesis Help in Chandigarh | Thesis Guidance in Chandigarh
Latest thesis topics in Machine Learning for research scholars:
Choosing a research and thesis topics in Machine Learning is the first choice of masters and Doctorate scholars now a days. Though, choosing and working on a thesis topic in machine learning is not an easy task as Machine learning uses certain statistical algorithms to make computers work in a certain way without being explicitly programmed. The algorithms receive an input value and predict an output for this by the use of certain statistical methods. The main aim of machine learning is to create intelligent machines which can think and work like human beings. Achieving the above mentioned goals is surely not very easy because of which students who choose research topic in machine learning face difficult challenges and require professional thesis help in their thesis work.
Below is the list of the latest thesis topics in Machine learning for research scholars:
- The classification technique for the face spoof detection in artificial neural networks using concepts of machine learning .
- The iris detection and reorganization system using classification and glcm algorithm in machine learning.
- Using machine learning algorithms in the detection of pattern system using algorithm of textual feature analysis and classification
- The plant disease detection using glcm and KNN classification in neural networks merged with the concepts of machine learning
- Using the algorithms of machine learning to propose technique for the prediction analysis in data mining
- The sentiment analysis technique using SVM classifier in data mining using machine learning approach
- The heart disease prediction using technique of classification in machine learning using the concepts of data mining.
So let’s start with machine learning.
First of all…
What exactly is machine learning?
Find the link at the end to download the latest topics for thesis and research in Machine Learning
What is Machine Learning?
Machine Learning is a branch of artificial intelligence that gives systems the ability to learn automatically and improve themselves from the experience without being explicitly programmed or without the intervention of human. Its main aim is to make computers learn automatically from the experience.
Requirements of creating good machine learning systems
So what is required for creating such machine learning systems? Following are the things required in creating such machine learning systems:
Data – Input data is required for predicting the output.
Algorithms – Machine Learning is dependent on certain statistical algorithms to determine data patterns.
Automation – It is the ability to make systems operate automatically.
Iteration – The complete process is iterative i.e. repetition of process.
Scalability – The capacity of the machine can be increased or decreased in size and scale.
Modeling – The models are created according to the demand by the process of modeling.
Methods of Machine Learning
Machine Learning methods are classified into certain categories These are:
- Supervised Learning
- Unsupervised Learning
Supervised Learning – In this method, input and output is provided to the computer along with feedback during the training. The accuracy of predictions by the computer during training is also analyzed. The main goal of this training is to make computers learn how to map input to the output.
Unsupervised Learning – In this case, no such training is provided leaving computers to find the output on its own. Unsupervised learning is mostly applied on transactional data. It is used in more complex tasks. It uses another approach of iteration known as deep learning to arrive at some conclusions.
Reinforcement Learning – This type of learning uses three components namely – agent, environment, action. An agent is the one that perceives its surroundings, an environment is the one with which an agent interacts and acts in that environment. The main goal in reinforcement learning is to find the best possible policy.
How does machine learning work?
Machine learning makes use of processes similar to that of data mining. Machine learning algorithms are described in terms of target function(f) that maps input variable (x) to an output variable (y). This can be represented as:
There is also an error e which is the independent of the input variable x. Thus the more generalized form of the equation is:
In machine the mapping from x to y is done for predictions. This method is known as predictive modeling to make most accurate predictions. There are various assumptions for this function.
Benefits of Machine Learning
Everything is dependent on machine learning. Find out what are the benefits of machine learning.
Decision making is faster – Machine learning provides the best possible outcomes by prioritizing the routine decision-making processes.
Adaptability – Machine Learning provides the ability to adapt to new changing environment rapidly. The environment changes rapidly due to the fact that data is being constantly updated.
Innovation – Machine learning uses advanced algorithms that improve the overall decision-making capacity. This helps in developing innovative business services and models.
Insight – Machine learning helps in understanding unique data patterns and based on which specific actions can be taken.
Business growth – With machine learning overall business process and workflow will be faster and hence this would contribute to the overall business growth and acceleration.
Outcome will be good – With machine learning the quality of the outcome will be improved with lesser chances of error.
Branches of Machine Learning
- Computational Learning Theory
- Adversarial Machine Learning
- Quantum Machine Learning
- Robot Learning
Computational Learning Theory – Computational learning theory is a subfield of machine learning for studying and analyzing the algorithms of machine learning. It is more or less similar to supervised learning.
Adversarial Machine Learning – Adversarial machine learning deals with the interaction of machine learning and computer security. The main aim of this technique is to look for safer methods in machine learning to prevent any form of spam and malware. It works on the following three principles:
Finding vulnerabilities in machine learning algorithms.
Devising strategies to check these potential vulnerabilities.
Implementing these preventive measures to improve the security of the algorithms.
Quantum Machine Learning – This area of machine learning deals with quantum physics. In this algorithm, the classical data set is translated into quantum computer for quantum information processing. It uses Grover’s search algorithm to solve unstructured search problems.
Predictive Analysis – Predictive Analysis uses statistical techniques from data modeling, machine learning and data mining to analyze current and historical data to predict the future. It extracts information from the given data. Customer relationship management(CRM) is the common application of predictive analysis.
Robot Learning – This area deals with the interaction of machine learning and robotics. It employs certain techniques to make robots to adapt to the surrounding environment through learning algorithms.
Grammar Induction – It is a process in machine learning to learn formal grammar from a given set of observations to identify characteristics of the observed model. Grammar induction can be done through genetic algorithms and greedy algorithms.
Meta-Learning – In this process learning algorithms are applied on meta-data and mainly deals with automatic learning algorithms.
Best Machine Learning Tools
Here is a list of artificial intelligence and machine learning tools for developers:
ai-one – It is a very good tool that provides software development kit for developers to implement artificial intelligence in an application.
Protege – It is a free and open-source framework and editor to build intelligent systems with the concept of ontology. It enables developers to create, upload and share applications.
IBM Watson – It is an open-API question answering system that answers questions asked in natural language. It has a collection of tools which can be used by developers and in business.
DiffBlue – It is another tool in artificial intelligence whose main objective is to locate bugs, errors and fix weaknesses in the code. All such things are done through automation.
TensorFlow – It is an open-source software library for machine learning. TensorFlow provides a library of numerical computations along with documentation, tutorials and other resources for support.
Amazon Web Services – Amazon has launched toolkits for developers along with applications which range from image interpretation to facial recognition.
OpenNN – It is an open-source, high-performance library for advanced analytics and is written in C++ programming language. It implements neural networks. It has a lot of tutorials and documentation along with an advanced tool known as Neural Designer.
Apache Spark – It is a framework for large-scale processing of data. It also provides a programming tool for deep learning on various machines.
Caffe – It is a framework for deep learning and is used in various industrial applications in the area of speech, vision and expression.
Veles – It is another deep learning platform written in C++ language and make use of python language for interaction between the nodes.
Machine Learning Applications
Following are some of the applications of machine learning:
Machine Learning in Bioinformatics
Bioinformatics term is a combination of two terms bio, informatics. Bio means related to biology and informatics means information. Thus bioinformatics is a field that deals with processing and understanding of biological data using computational and statistical approach. Machine Learning has a number of applications in the area of bioinformatics. Machine Learning find its application in the following subfields of bioinformatics:
Genomics – Genomics is the study of DNA of organisms. Machine Learning systems can help in finding the location of protein-encoding genes in a DNA structure. Gene prediction is performed by using two types of searches named as extrinsic and intrinsic. Machine Learning is used in problems related to DNA alignment.
Proteomics – Proteomics is the study of proteins and amino acids. Proteomics is applied to problems related to proteins like protein side-chain prediction, protein modeling, and protein map prediction.
Microarrays – Microarrays are used to collect data about large biological materials. Machine learning can help in the data analysis, pattern prediction and genetic induction. It can also help in finding different types of cancer in genes.
System Biology – It deals with the interaction of biological components in the system. These components can be DNA, RNA, proteins and metabolites. Machine Learning help in modeling these interactions.
Text mining – Machine learning help in extraction of knowledge through natural language processing techniques.
Deep Learning is a part of the broader field machine learning and is based on data representation learning. It is based on the interpretation of artificial neural network. Deep Learning algorithm uses many layers of processing. Each layer uses the output of previous layer as an input to itself. The algorithm used can be supervised algorithm or unsupervised algorithm. Deep Learning is mainly developed to handle complex mappings of input and output. It is another hot topic for M.Tech thesis and project along with machine learning.
Deep Neural Network
Deep Neural Network is a type of Artificial Neural Network with multiple layers which are hidden between the input layer and the output layer. This concept is known as feature hierarchy and it tends to increase the complexity and abstraction of data. This gives network the ability to handle very large, high-dimensional data sets having millions of parameters. The procedure of deep neural networks is as follows:
Consider some examples from a sample dataset.
Calculate error for this network.
Improve weight of the network to reduce the error.
Repeat the procedure.
Applications of Deep Learning
Here are some of the applications of Deep Learning:
Automatic Speech Recognition
Natural Language Processing
Customer Relationship Management
Advantages of Deep Learning
Deep Learning helps in solving certain complex problems with high speed which were earlier left unsolved. Deep Learning is very useful in real world applications. Following are some of the main advantages of deep learning:
Eliminates unnecessary costs – Deep Learning helps to eliminate unnecessary costs by detecting defects and errors in the system.
Identifies defects which otherwise are difficult to detect – Deep Learning helps in identifying defects which left untraceable in the system.
Can inspect irregular shapes and patterns – Deep Learning can inspect irregular shapes and patterns which is difficult for machine learning to detect.
From this introduction, you must have known that why this topic is called as hot for your M.Tech thesis and projects. This was just the basic introduction to machine learning and deep learning. There is more to explore in these fields. You will get to know more once you start doing research on this topic for your M.Tech thesis. You can get thesis assistance and guidance on this topic from experts specialized in this field.
Research and Thesis Topics in Machine Learning
Here is the list of current research and thesis topics in Machine Learning :
Machine Learning Algorithms
Supervised Machine Learning
Unsupervised Machine Learning
For starting with Machine Learning, you need to know some algorithms. Machine Learning algorithms are classified into three categories which provide the base for machine learning. These categories of algorithms are supervised learning, unsupervised learning, and reinforcement learning. The choice of algorithms depends upon the type of tasks you want to be done along with the type, quality, and nature of data present. The role of input data is crucial in machine learning algorithms.
Computer Vision is a field that deals with making systems that can read and interpret images. In simple terms, computer vision is a method of transmitting human intelligence and vision in machines. In computer vision, data is collected from images which are imparted to systems. The system will take action according to the information it interprets from what it sees.
It is a good topic for machine learning masters thesis. It is a type of machine learning algorithm in which makes predictions based on known data-sets. Input and output is provided to the system along with feedback. Supervised Learning is further classified into classification and regression problems. In the classification problem, the output is a category while in regression problem the output is a real value.
It is another category of machine learning algorithm in which input is known but the output is not known. Prior training is not provided to the system as in case of supervised learning. The main purpose of unsupervised learning is to model the underlying structure of data. Clustering and Association are the two types of unsupervised learning problems. k-means and Apriori algorithm are the examples of unsupervised learning algorithms.
Deep Learning is a hot topic in Machine Learning. It is already explained above. It is a part of the family of machine learning and deals with the functioning of the artificial neural network. Neural Networks are used to study the functioning of the human brain. It is one of the growing and exciting field. Deep learning has made it possible for the practical implementation of various machine learning applications.
Neural Networks are the systems to study the biological neural networks. It is an important application of machine learning and a good topic for masters thesis and research. The main purpose of Artificial Neural Network is to study how the human brain works. It finds its application in computer vision, speech recognition, machine translation etc. Artificial Neural Network is a collection of nodes which represent neurons.
Reinforcement Learning is a category of machine learning algorithms. Reinforcement Learning deals with software agents to study how these agents take actions in an environment in order to maximize their performance. Reinforcement Learning is different from supervised learning in the sense that correct input and output parameters are not provided.
Predictive Learning is another good topic for thesis in machine learning. In this technique, a model is built by an agent of its environment in which it performs actions. There is another field known as predictive analytics which is used to make predictions about future events which are unknown. For this, techniques like data mining, statistics, modeling, machine learning, and artificial intelligence are used.
It is a network that represents probabilistic relationships via Directed Acyclic Graph(DAG). There are algorithms in Bayesian Network for inference and learning. In the network, a probability function is there for each node which takes an input to give probability to the value associated with the node. Bayesian Network finds its application in bioinformatics, image processing, and computational biology.
Data Mining is the process of finding patterns from large data-sets to extract valuable information to make better decisions. It is a hot area of research. This technology use method from machine learning, statistics, and database systems for processing. There exist data mining techniques like clustering, association, decision trees, classification for the data mining process.
Click on the following link to download the latest thesis and research topics in Machine Learning
Latest Thesis and Research Topics on Machine Learning(pdf)
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