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Course Detail

Course Name Machine Learning
Course Code 23DLS513
Semester 2
Credits 4



Introduction: Well-Posed Learning Problems, Designing a Learning System. The Inductive Learning Hypothesis and Concept Learning as Search (definitions). Machine Learning Basics: Learning Algorithms, Capacity, Over fitting and Under fitting , Hyper parameters and Validation Sets , No Free Lunch theorem, Estimators, Bias and Variance , Bayesian statistics, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Stochastic Gradient Descent , Building a Machine Learning Algorithm and Issues in Machine Learning.
Decision Tree learning: Introduction, Decision tree representation, Appropriate problems for decision tree learning, The basic decision tree learning algorithm, Hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning. Implementation aspects of the Decision Tree and Classification Example.


Instance-Based Learning: Introduction, k-Nearest Neighbour Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based Reasoning, Remarks on Lazy and Eager Learning.

Support Vector Machines: Optimal Separation: The Margin and Support Vectors, a Constrained Optimization Problem, Slack Variables for Non-Linearly Separable Problems. KERNELS: Choosing Kernels. The Support Vector Machine Algorithm and Multi-Class Classification. Case Study.


Genetic Algorithms: Motivation, Genetic Algorithms, Elitism, Tournaments, and Niching, Using Genetic Algorithms and An illustrative Example. Hypothesis Space Search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.
Reinforcement Learning: Introduction, the Learning Task, Q Learning, Non-Deterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming. Case Study.

Text Books

1. Machine Learning – Tom M. Mitchell, – MGH
2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)
3. Haroon, D. (2017). Python Machine Learning Case Studies: Five Case Studies for the Data Scientist Apress.

Reference Books

1. Harrington, P. (2012). Machine learning in action. Manning Publications Co..
2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & Sons
3. Machine Learning by Peter Flach , Cambridge.

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