MACHINE LEARNING
(Program Elective – I)
Prerequisites
1. A Course on ”Data Structures”
2. Knowledge on statistical methods
Objectives
1. This course explains machine learning techniques such as decision tree learning,
Bayesian learning etc.
2. To understand computational learning theory.
3. To study the pattern comparison techniques.
Outcomes
1. Understand the concepts of computational intelligence like machine learning
2. Ability to get the skill to apply machine learning techniques to address the real time
problems in different areas
3. Understand the Neural Networks and its usage in machine learning application.
UNIT – I
Introduction
Well-posed learning problems, designing a learning system Perspectives and issues in machine
learning
Concept learning and the general to specific ordering
Introduction, 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.
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.
UNIT – II
Artificial Neural Networks
Introduction, Neural Network Representation, Appropriate Problems for Neural Network
Learning, Perceptions, Multilayer Networks and the Back propagation Algorithm.
Discussion on the Back Propagation Algorithm, An illustrative Example: Face Recognition
Evaluation Hypotheses
Motivation, Estimation Hypothesis Accuracy, Basics of Sampling Theory, A General
Approach for Deriving Confidence Intervals, Difference in Error of Two Hypotheses,
Comparing Learning Algorithms.
UNIT – III
Bayesian learning
Introduction, Bayes Theorem, Bayes Theorem and Concept Learning Maximum Likelihood
and Least Squared Error Hypotheses, Maximum Likelihood Hypotheses for Predicting
Probabilities, Minimum Description Length Principle , Bayes Optimal Classifier, Gibs
Algorithm, Naïve Bayes Classifier, An Example: Learning to Classify Text, Bayesian Belief
Networks, EM Algorithm.
Computational Learning TheoryIntroduction, Probably Learning an Approximately Correct Hypothesis, Sample Complexity
for Finite Hypothesis Space, Sample Complexity for Infinite Hypothesis Spaces, The Mistake
Bound Model of Learning.
Instance-Based Learning
Introduction, k-Nearest Neighbor Learning, Locally Weighted Regression, Radial Basis
Functions, Case-Based Reasoning, Remarks on Lazy and Eager Learning.
UNIT – IV
Pattern Comparison Techniques
Temporal patterns, Dynamic Time Warping Methods, Clustering, Codebook Generation,
Vector Quantization
Pattern Classification
Introduction to HMMS, Training and Testing of Discrete Hidden Markov Models and
Continuous Hidden Markov Models, Viterbi Algorithm, Different Case Studies in Speech
recognition and Image Processing
UNIT – V
Analytical Learning
Introduction, Learning with Perfect Domain Theories : PROLOG-EBG Remarks on
Explanation-Based Learning, Explanation-Based Learning of Search Control Knowledge,
Using Prior Knowledge to Alter the Search Objective, Using Prior Knowledge to Augment
Search Operations.
Combining Inductive and Analytical Learning
Motivation, Inductive-Analytical Approaches to Learning, Using Prior Knowledge to Initialize
the Hypothesis.
Textbooks:
1. Machine Learning – Tom M.Mitchell,-MGH
2. Fundamentals of Speech Recognition By Lawrence Rabiner and Biing – Hwang Juang.
References
1. Machine Learning : An Algorithmic Perspective, Stephen Marsland, Taylor & Francis