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 – Introduction to Reinforcement Learning.

Pre-Requisite(s):Basics of Linear Algebra, Probability Theory and Optimization: Vectors, Inner product, Outer product, Inverse of a matrix, Eigenanalysis, Probability distributions – Discrete distributions and Continuous distributions; Independence of events, Conditional probability distribution and Joint probability distribution, Bayes theorem, Unconstrained optimization, Constrained optimization.
Course Type: Lab

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

CO1: Understand and apply the basic of ML, learning paradigms and concepts of regression
CO2: Design and develop classifier models and evaluate their performance
CO3: Acquire skills to build probabilistic model and deep network models for classification
CO4: Develop and build clustering models for real world applications
CO5: Understand and apply the concepts of dimensionality reduction and Reinforcement Learning

CO-PO Mapping

Evaluation Pattern: 70/30

Note: Continuous assessments can include quizzes, tutorials, lab assessments, case study and project reviews. Midterm and End semester exams can be a theory exam or lab integrated exam for two hours

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.