Syllabus

Introduction to machine learning – different forms of learning- Linear regression – Ridge and Lasso regression, Logistic regression, Discriminant Functions and models, Bayesian regression, regression with basic functions.

Classification – Perceptron –Multilayer Perceptron – Feed forward network – Backpropagation – Support vector machine – Decision trees – evaluation of classifiers – bias and variance. Gaussian mixture models — Expectation-Maximization – Naive Bayes classifier – Ensemble Methods – Bagging – Boosting -Time series Prediction and Markov Process – Introduction to deep learning – Convolutional neural networks – application of classification algorithm

Clustering – K-means – Hierarchical and Density Based Clustering – DBSCAN- Assessing Quality of Clustering – Dimensionality reduction – Principal Component Analysis. Understanding of Machine Learning Project Life Cycle – each stages of the cycle, Examples of ML projects.

Preamble

This course deals with various algorithms to enable computers to learn data without being explicitly programmed. An insight into various types of machine learning algorithms, strategies for model generation and evaluation are given in this course. The fundamental machine learning algorithms required in industries are covered together with their concrete implementations.

Course Objectives

• To introduce the fundamental concepts and techniques of Machine Learning
• To become familiar with various classification and regression methods
• Apply neural networks, Bayes classifier and k nearest neighbor algorithms in machine learning.
• To develop skills using recent machine learning techniques and solving real world case study

Course Outcomes

CO-PO Mapping

Prerequisites

• Basics of Linear Algebra, Probability Theory and Optimization: Vectors, Inner product, Outer product, Inverse of a matrix, Eigen analysis, Probability distributions – Discrete distributions and Continuous distributions; Independence of events, Conditional probability distribution and Joint probability distribution, Bayes theorem, Unconstrained optimization, Constrained optimization.

Evaluation Pattern – 70:30

• Midterm Exam – 20%
• Continuous Evaluation – 50%
• End Semester Exam – 30%

Text Book / References

1. Alpaydin, Ethem. Introduction to machine learning. MIT press, 2020
2. Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
3. Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
4. Tom Mitchell, “Machine Learning”, McGraw-Hill, 1997
5. Duda, Richard O., and Peter E. Hart. Pattern classification. John Wiley & Sons, 2006.
6. Han, Jiawei, Micheline Kamber, and Jian Pei. “Data mining concepts and techniques third edition.” The Morgan Kaufmann Series in Data Management Systems 5.4 (2011): 83-124.