Machine Learning - II BAI702

Module 1 Overview

Introduction: Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning.

Concept Learning and the General-to-Specific Ordering: A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate-Elimination Algorithm, Remarks on Version Spaces and Candidate-Elimination, Inductive Bias.

Text Book 1 : Ch 1 & 2

Module 2 Overview

Learning Sets of Rules: Sequential Covering Algorithms, Learning Rule Sets: Example-Based Methods, Learning First-Order Rules, FOIL: A First-Order Inductive Learner.

Analytical Learning: Perfect Domain Theories: Explanation-Based Learning, Explanation-Based Learning of Search Control Knowledge, Inductive-Analytical Approaches to Learning.

Text Book 1 : Ch 10 & 11

Module 3 Overview

Decision by Committee: Ensemble Learning: Boosting: Adaboost, Stumping, Bagging: Subagging, Random Forests, Comparison With Boosting, Different Ways To Combine Classifiers.

Unsupervised Learning: The K-MEANS algorithm: Dealing with Noise, The k-Means Neural Network, Normalisation, A Better Weight Update Rule, Using Competitive Learning for Clustering.

Text Book 2: Chap 13 and 14.1

Module 4 Overview

Unsupervised Learning: Vector Quantisation, the self-organising feature map, The SOM Algorithm, Neighbourhood Connections, Self-Organisation, Network Dimensionality and Boundary Conditions, Examples of Using the SOM.

Markov Chain Monte Carlo (MCMC) Methods: Sampling: Random Numbers, Gaussian Random Numbers, Monte Carlo Or Bust, The Proposal Distribution, Markov Chain Monte Carlo.

Text Book 2: Chap 14.2, 14.3, 15

Module 5 Overview

Graphical Models: Bayesian Networks: Approximate Inference, Making Bayesian Networks, Markov Random Fields, Hidden Markov Models (Hmms), The Forward Algorithm, The Viterbi Algorithm, The Baum - Welch Or Forward - Backward Algorithm, Tracking Methods, The Kalman Filter, The Particle Filter.

Text Book 2 : Chap 16

Machine Learning - II BAI702 is a VTU course covered through module-wise syllabus, notes, and PYQ-driven exam practice available on this page.

Credits for BAI702: 04.

Yes. You can access organized notes, PDFs, and PYQ material from the file explorer/resources section on this page.

Start with module summaries, solve recent PYQs unit-wise, and finish with complete paper practice under time constraints for SEE readiness.

Yes, this page is maintained with current scheme-oriented materials and practical exam-focused resource curation.

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