Course

Professional Electives

Other Branches

Unit I

Foundations of machine learning – Training, Validation and Testing, Problem of Overfitting & Underfitting, Bias vs Variance, Performance metrics – Regression Vs Classification, Linear Regression, Logistic Regression, Decision Tree, Random Forest, Perceptron, Beyond binary classification.

Unit II

Advanced supervised learning – Naive Bayes, Bayesian Belief Network, K-Nearest Neighbor, Support vector machines, Markov model, Hidden Markov Model, Neural Networks.

Unit III

Unsupervised Learning: Curse of Dimensionality, Dimensionality Reduction Techniques, Principal component analysis, Linear Discriminant Analysis Clustering: K-means, Hierarchical, Spectral, subspace clustering, association rule mining.

Pre-Requisite(s): Linear Algebra, Probability and Random Processes

Course Objectives

• To provide in-depth knowledge of machine learning.
• To facilitate students to learn to implement, train and validate the machine learning models and understand the recent algorithms in machine learning through case studies.

Course Outcomes

CO1: Understand the issues and challenges in machine learning: data, model selection, model complexity

CO2: Design various machine learning algorithms and understand the importance of training and testing the data.

CO3: Design perfect algorithm with the steps involved in designing a machine learning system.

CO4: Analyze the mathematical relationships and identify appropriate Machine Learning algorithms for real world

applications. 

CO5: Apply the paradigms of supervised and un-supervised learning based on the problem statement.

CO-PO Mapping

Evaluation Pattern: 70:30

*CAT – Can be Quizzes, Assignments, and Reports

*CAL – Can be Lab Assessments, Project, and Report

**End Semester can be theory examination/ lab-based examination/ project presentation

Textbook(s)

Tom Mitchell. “Machine Learning”. McGraw Hill; 2017.

Reference(s)

Christopher M Bishop. “Pattern Recognition and Machine Learning”. Springer 2010.

Richard O. Duda, Peter E. Hart, David G. Stork. “Pattern Classification”. Wiley, Second Edition;2007.

Kevin P. Murphey. “Machine Learning, a probabilistic perspective”. The MIT Press Cambridge, Massachusetts, 2012.