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Course Detail

Course Name Machine Learning lab with Python
Course Code 19EAC381
Program B. Tech. in Electronics and Computer Engineering
Semester 5
Year Taught 2019


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.

Objectives and Outcomes

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

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO1 1 1 1
CO2 1 3 2 1 1 3
CO3 1 2 3 2 3
CO4 1 2 3 2 2 3

Textbook / References

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)

Assessment Internal External
*Continuous Assessment (CA) 80
End Semester 20
*CA – Can be Quizzes, Assignment, Projects, and Reports.

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