1. Introduction to Python- Importing datasets- Data visualization.

2. Lab experiments demonstrating Dimensionality Reduction, Regression, Discriminant analysis, SVM, Gaussian Mixture models, k-Nearest Neighbor Classification, Naive Bayes classification, K- Means clustering, Hidden Markov models (HMMs)

3. Case Study involving classification including document classification or with applications like recommendation systems, advertising on the web, using ML tools.

Course Objectives

• To understand the basic concepts and techniques of Machine Learning through python programming.
• To develop skills of using recent machine learning packages for solving practical problems.
• To gain experience of doing independent study and research.

Course Outcomes

• CO1: Familiarize Python
• CO2: Able to generate, analyze and interpret data using Python.
• CO3: Use Python to design and implement classifiers for machine learning applications.
• CO4: Implement an end to end Machine Learning System

CO – PO Mapping

Textbook / References

• C. M. Bishop. Pattern Recognition and Machine Learning. First Edition. Springer, 2006. (Second Indian Reprint, 2015).
• P. Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. First Edition, Cambridge University Press, 2012.
• S. J. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. Third Edition, Prentice-Hall, 2010.
• Y. S. Abu-Mostafa, M. Magdon-Ismail, H.-T. Lin. Learning from Data: A Short Course. First Edition, 2012.

Evaluation Pattern 80:20 (Internal: External)