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Exploring the Limitations and Challenges of Machine Learning Technology
Machine learning is a subset of artificial intelligence (AI) that involves developing algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. While machine learning has seen significant advancements in recent years, it still faces several limitations and challenges that must be addressed to unlock its full potential.
Data Quality and Quantity
One of the biggest challenges facing machine learning is the quality and quantity of data available. Machine learning models rely heavily on large amounts of high-quality data to learn patterns, make predictions, and improve accuracy over time. However, many organizations struggle with collecting, cleaning, and storing large amounts of data in a usable format. Additionally, the lack of diversity in datasets can lead to biased results.
Interpretability
Another limitation of machine learning is its lack of interpretability. Many complex machine learning models are considered “black boxes” because it is difficult for humans to understand how they arrived at a particular decision or prediction. This can be problematic in industries such as healthcare or finance where transparency and accountability are critical.
Algorithmic Bias
Machine learning algorithms are only as unbiased as the data they are trained on. If a dataset contains inherent biases or prejudices, then the resulting algorithm will also be biased. This can lead to unfair treatment or discrimination against certain groups.
Computational Complexity
Some machine learning algorithms require significant computational resources to train effectively. This can limit their scalability and practical applications in real-world scenarios where time and resources are limited.
Despite these limitations and challenges, machine learning remains a powerful tool for businesses across various industries. As technology continues to evolve, it is important for developers to address these concerns through responsible AI development practices such as ethical considerations during model development, diverse dataset collection practices, interpretability techniques such as explainable AI (XAI), continuous monitoring for algorithmic bias detection during model deployment, optimization techniques for improving computational efficiency etc.
In conclusion, machine learning technology has come a long way and offers significant potential for businesses and industries. By acknowledging and addressing its limitations and challenges, we can ensure that it is used ethically, responsibly, and effectively to drive innovation and progress.
This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.
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Machine Learning - CMU

PhD Dissertations
[all are .pdf files].
Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023
Collaborative learning by leveraging siloed data Sebastian Caldas, 2023
Modeling Epidemiological Time Series Aaron Rumack, 2023
Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023
Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023
Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023
Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023
Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023
Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023
Applied Mathematics of the Future Kin G. Olivares, 2023
METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023
NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023
Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023
Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang
Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology (Unavailable) Darby M. Losey, 2023
Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023
Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022
Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022
Making Scientific Peer Review Scientific Ivan Stelmakh, 2022
Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022
Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022
Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022
Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022
Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022
Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022
Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022
Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021
Neural variability: structure, sources, control, and data augmentation (Unavailable) Akash Umakantha, 2021
Structure and time course of neural population activity during learning (Unavailable) Jay Hennig, 2021
Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021
Meta Reinforcement Learning through Memory Emilio Parisotto, 2021
Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021
Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021
Statistical Game Theory Arun Sai Suggala, 2021
Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021
Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021
Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021
Curriculum Learning Otilia Stretcu, 2021
Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021
Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021
Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021
Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021
Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021
Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020
Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020
Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020
Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020
Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020
Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020
Learning DAGs with Continuous Optimization Xun Zheng, 2020
Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020
Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020
Towards Data-Efficient Machine Learning Qizhe Xie, 2020
Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020
Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020
Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020
Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020
Towards Efficient Automated Machine Learning Liam Li, 2020
LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020
Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020
Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020
Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020
Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019
Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019
Estimating Probability Distributions and their Properties Shashank Singh, 2019
Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019
Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019
Multi-view Relationships for Analytics and Inference (unavailable) Eric Lei, 2019
Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019
Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019
The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019
Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019
Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019
Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019
Unified Models for Dynamical Systems Carlton Downey, 2019
Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019
Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019
Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019
New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019
Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019
Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019
Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019
Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018
Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018
Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018
Statistical Inference for Geometric Data Jisu Kim, 2018
Representation Learning @ Scale Manzil Zaheer, 2018
Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018
Distribution and Histogram (DIsH) Learning Junier Oliva, 2018
Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018
Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018
Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018
Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018
Learning with Staleness Wei Dai, 2018
Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017
New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017
Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017
New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017
Active Search with Complex Actions and Rewards Yifei Ma, 2017
Why Machine Learning Works George D. Montañez , 2017
Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017
Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016
Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016
Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016
Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016
Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016
Combining Neural Population Recordings: Theory and Application William Bishop, 2015
Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015
Machine Learning in Space and Time Seth R. Flaxman, 2015
The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015
Shape-Constrained Estimation in High Dimensions Min Xu, 2015
Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015
Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015
Learning Statistical Features of Scene Images Wooyoung Lee, 2014
Towards Scalable Analysis of Images and Videos Bin Zhao, 2014
Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014
Modeling Large Social Networks in Context Qirong Ho, 2014
Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013
On Learning from Collective Data Liang Xiong, 2013
Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013
Mathematical Theories of Interaction with Oracles Liu Yang, 2013
Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013
Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013
Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013
Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013
Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013
GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013
Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)
Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013
Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013
New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)
Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012
Spectral Approaches to Learning Predictive Representations Byron Boots, 2012
Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012
Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012
Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012
Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012
Target Sequence Clustering Benjamin Shih, 2011
Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)
Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010
Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010
Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010
Rare Category Analysis Jingrui He, 2010
Coupled Semi-Supervised Learning Andrew Carlson, 2010
Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009
Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009
Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009
Theoretical Foundations of Active Learning Steve Hanneke, 2009
Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009
Detecting Patterns of Anomalies Kaustav Das, 2009
Dynamics of Large Networks Jurij Leskovec, 2008
Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008
Stacked Graphical Learning Zhenzhen Kou, 2007
Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007
Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007
Scalable Graphical Models for Social Networks Anna Goldenberg, 2007
Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007
Tools for Graph Mining Deepayan Chakrabarti, 2005
Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

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Home > Dissertations and Theses > Computational and Data Sciences (PhD) Dissertations
Computational and Data Sciences (PhD) Dissertations
Below is a selection of dissertations from the Doctor of Philosophy in Computational and Data Sciences program in Schmid College that have been included in Chapman University Digital Commons. Additional dissertations from years prior to 2019 are available through the Leatherby Libraries' print collection or in Proquest's Dissertations and Theses database.
Dissertations from 2023 2023
Computational Analysis of Antibody Binding Mechanisms to the Omicron RBD of SARS-CoV-2 Spike Protein: Identification of Epitopes and Hotspots for Developing Effective Therapeutic Strategies , Mohammed Alshahrani
Integration of Computer Algebra Systems and Machine Learning in the Authoring of the SANYMS Intelligent Tutoring System , Sam Ford
Voluntary Action and Conscious Intention , Jake Gavenas
Computational Modeling of Superconductivity from the Set of Time-Dependent Ginzburg-Landau Equations for Advancements in Theory and Applications , Iris Mowgood
Application of Machine Learning Algorithms for Elucidation of Biological Networks from Time Series Gene Expression Data , Krupa Nagori
Stochastic Processes and Multi-Resolution Analysis: A Trigonometric Moment Problem Approach and an Analysis of the Expenditure Trends for Diabetic Patients , Isaac Nwi-Mozu
Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis , Albert Pierce
Causal Inference Methods for Estimation of Survival and General Health Status Measures of Alzheimer’s Disease Patients , Ehsan Yaghmaei
Dissertations from 2022 2022
Computational Approaches to Facilitate Automated Interchange between Music and Art , Rao Hamza Ali
Causal Inference in Psychology and Neuroscience: From Association to Causation , Dehua Liang
Advances in NLP Algorithms on Unstructured Medical Notes Data and Approaches to Handling Class Imbalance Issues , Hanna Lu
Novel Techniques for Quantifying Secondhand Smoke Diffusion into Children's Bedroom , Sunil Ramchandani
Probing the Boundaries of Human Agency , Sook Mun Wong
Dissertations from 2021 2021
Predicting Eye Movement and Fixation Patterns on Scenic Images Using Machine Learning for Children with Autism Spectrum Disorder , Raymond Anden
Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models , Alexander Barrett
Applications of Machine Learning to Facilitate Software Engineering and Scientific Computing , Natalie Best
Exploring Behaviors of Software Developers and Their Code Through Computational and Statistical Methods , Elia Eiroa Lledo
Assessing the Re-Identification Risk in ECG Datasets and an Application of Privacy Preserving Techniques in ECG Analysis , Arin Ghazarian
Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking , Nicholas LaHaye
Machine-Learning-Based Approach to Decoding Physiological and Neural Signals , Elnaz Lashgari
Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions , Justin Le
Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning , Shiva Lotfallahzadeh Barzili
Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures , Chelsea Parlett
Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data , Jianwei Zheng
Dissertations from 2020 2020
Development of Integrated Machine Learning and Data Science Approaches for the Prediction of Cancer Mutation and Autonomous Drug Discovery of Anti-Cancer Therapeutic Agents , Steven Agajanian
Allocation of Public Resources: Bringing Order to Chaos , Lance Clifner
A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay , Sidy Danioko
A Computational and Experimental Examination of the FCC Incentive Auction , Logan Gantner
Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning , Kayleigh Hyde
Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations , Oluyemi Odeyemi
On Quantum Effects of Vector Potentials and Generalizations of Functional Analysis , Ismael L. Paiva
Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability , Luciano Rodriguez
Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder , Natalia Rosenfield
Connecting the Dots for People with Autism: A Data-driven Approach to Designing and Evaluating a Global Filter , Viseth Sean
Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance , Christopher Watkins

Dissertations from 2019 2019
Contributions to Variable Selection in Complexly Sampled Case-control Models, Epidemiology of 72-hour Emergency Department Readmission, and Out-of-site Migration Rate Estimation Using Pseudo-tagged Longitudinal Data , Kyle Anderson
Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images , Justin J. Gapper
Estimating Auction Equilibria using Individual Evolutionary Learning , Kevin James
Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs , Wenzhao Li
Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique , Chloe Martin-King
Theses from 2017 2017
Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods , Michael Schwartz
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Latest phd thesis topics in machine learning.

- With progressive technological development, the exploration of machine learning has increased a huge number of applications. Consequentially. Machine learning instigates an essential part of implementing smart and automated applications by intelligent data analysis.
- The applicability of machine learning is abundant in many real-world application fields, such as predictive analytics and intelligent decision-making, cyber-security systems, smart cities, healthcare, e-commerce, agriculture, finance, retail, social media, traffic prediction and transportation, computer vision applications, user behavior analytics and context-aware smartphone applications, bioinformatics, cheminformatics, computer networks, DNA sequence classification, economics and banking, robotics, advanced engineering, and many more.
- Recently described branches of machine learning are computational learning theory, adversarial machine learning, quantum machine learning, robot learning, and meta-learning. Efficient data processing and handling the diverse learning algorithms are the constraints that are needed to be the focus in machine learning. PHD thesis on machine learning contributes to the challenges, promising research opportunities, and effective solutions in various application areas. Below is the list of PHD thesis topics on machine learning to effectively explore, scrutinize and discover the new findings of machine learning systems.
List of Sample PHD Thesis in Machine Learning
- Incremental Learning for Large-Scale Data Stream Analytics in a Complex Environment
- Neural Sequential Transfer Learning for Relation Extraction
- Predicting Depression and Suicide Ideation in the Canadian Population Using Social Media Data
- Neural Aspect-based Text Generation
- Leveraging Social Media and Machine Learning for enhanced Communication and Understanding between Organizations and Stakeholders
- Deep Learning Methods for Short,Informal, and Multilingual Text Analytics
- Deep Learning Based Cursive Text Detection and Recognition in Natural Scene Images
- Deep Learning-Based Text Detection and Recognition
- Explaining Deep Neural Networks
- Machine Learning Techniques in Spam Filtering
- Anomaly-Based Network Intrusion Detection Using Machine Learning
- Machine Learning for Financial Products Recommendation
- Sentiment Analysis of Textual Content in Social Networks
- Deep Learning For Time Series Classification
- Deep Learning for Traffic Time Series Data Analysis
- Novel applications of Machine Learning to Network Traffic Analysis and Prediction
- Deep Learning for Animal Recognition
- Neural Transfer Learning for Natural Language Processing
- Scalable and Ensemble Learning for Big Data
- Ensembles for Time Series Forecasting
- Sample-Efficient Deep Reinforcement Learning for Continuous Control
- Towards Generalization and Efficiency in Reinforcement Learning
- Transfer Learning with Deep Neural Networks for Computer Vision
- Deep Learning for Recommender Systems
- CHAMELEON: A Deep Learning Meta-Architecture For News Recommender Systems
- Learning in Dynamic Data-Streams with a Scarcity of Labels
- Learning Meaning Representations For Text Generation With Deep Generative Models
- Social Media Sentiment Analysis with a Deep Neural Network: An Enhanced Approach Using User Behavioral Information
- Global-Local Word Embedding for Text Classification
- Measuring Generalization and Overfitting in Machine Learning
- Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions
- Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn
- Deep Learning Based Imbalanced Data Classification and Information Retrieval for Multimedia Big Data
- Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data
- An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains
- Deep Learning Applications for Biomedical Data and Natural Language Processing
- Deep Neural Network Models for Image Classification and Regression
- Deep learning for medical report texts
- Deep multi-agent reinforcement learning
- Artificial intelligence methods to support people management in organisations
- An Intelligent Recommender System Based on Short-term Disease Risk Prediction for Patients with Chronic Diseases in a Telehealth Environment
- Bringing Interpretability and Visualization with Artificial Neural Networks
- Investigating machine learning methods in Recommender systems
- Adaptive Machine Learning Algorithms For Data Streams Subject To Concept Drifts
- Active Learning for Data Streams
- Heart Diseases Diagnosis Using Artificial Neural Networks
- Advanced Natural Language Processing and Temporal Mining for Clinical Discovery
- Uncertainty in Deep Learning
- Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi-Agent Systems
- Sentiment analysis on students Real-time Feedback
- Aspect-Based Opinion Mining From Customer Reviews
- Word Embeddings for Natural Language Processing
- On Effectively Creating Ensembles of Classifiers
- Design of Intelligent Ensembled Classifiers Combination Methods
- ELSE: Ensemble Learning System with Evolution for Content Based Image Retrieval
- Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks
- Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks
- Using Assessments of Contextual Learning to Identify Characteristics of Adaptive Transfer in Medical Students
- Recursive Deep Learning for Natural Language Processing and Computer Vision
- Machine learning strategies for multi-step-ahead time series forecasting
- General Attention Mechanism for Artificial Intelligence Systems
- Defense Acquisition University: A Study of Employee Perceptions on Web Based Learning Transfer
- Incremental Learning with Large Datasets
- Machine Learning and Data Mining Methods for Recommender Systems and Chemical Informatics
- Transfer of Learning in Leadership Development: Lived Experiences of HPI Practitioners
- Online Ensemble Learning in the Presence of Concept Drift
- Learning From Data Streams With Concept Drift
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Machine learning applications in finance: some case studies
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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
Reinforcement 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
- Meta-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:
Cognitive Services
Medical Services
Language Processing
Business Management
Image Recognition
Face Detection
Video Games
Computer Vision
Pattern Recognition
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

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
Bioinformatics
Mobile Advertising
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
Neural Networks
Predictive Learning
Bayesian Network
Data Mining
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.
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Latest Thesis and Research Topics on Machine Learning(pdf)
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