18EC745 Machine Learning syllabus for EC



A d v e r t i s e m e n t

Module-1 Introduction 8 hours

Introduction:

Well posed learning problems, Designing a Learning system, Perspective and Issues in Machine Learning.

 

Concept Learning:

Concept learning task, Concept learning as search, Find-S algorithm, Version space, Candidate Elimination algorithm, Inductive Bias.

 

Python libraries suitable for Machine Learning:

Numerical Analysis and Data Exploration with NumPy Arrays, and Data Visualization with Matplotlib Text Book1, Sections: 1.1-1.3,2.1-2.5, 2.7

Module-2 Decision Tree Learning 8 hours

Decision Tree Learning:

Decision tree representation, Appropriate problems for decision tree learning, Basic decision tree learning algorithm, hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning. Example program in Python Text Book1, Sections: 3.1-3.7

Module-3 Artificial Neural Networks 8 hours

Artificial Neural Networks :

Introduction, Neural Network representation, Appropriate problems, Perceptrons, Back propagation algorithm. Example program in Python Textbook1,Sections: 4.1-4.6

Module-4 Bayesian Learning 8 hours

Bayesian Learning:

Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting probabilities, MDL principle, Naive Bayes classifier, Bayesian belief networks, EM algorithm, Example program in Python. Text book 1,Sections: 6.1- 6.6, 6.9, 6.11, 6.12

Module-5 Evaluating Hypothesis 8 hours

Evaluating Hypothesis:

Motivation, Estimating hypothesis accuracy, Basics of sampling theorem, General approach for deriving confidence intervals, Differena: in error oftwo hypothesis, Comparing learning algorithms.

 

Instance Based Learning:

Introduction, k-nearest neighbor learning, locally weighted regression, radial basis function, cased-based reasoning,

 

Reinforcement Learning:

Introduction, Learning Task, Q I..eaming Example program in Python. Text book 1,Sections: 5.1-5.6, 8.1-8.5, 13.1-13.3 

 

Course Outcomes:

After studying this course, students will be able to

1. Identify the problems in machine learning.

2. Select supervised, unsupervised or reinforcement learning for problem solving.

3. Apply theory of probability and statistics in machine learning

4. Apply concept learning, ANN, Bayes classifier, k nearest neighbor

5. Perform statistical analysis of machine learning techniques.

 

Question paper pattern:

  • The question paper will have ten questions.
  • There will be 2 questions from each module.
  • Each question will have questions covering all the topics under a module.
  • The students will have to answer 5 full questions, selecting one full question from each module.

 

Text Books:

1. Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education.

 

Reference Books:

1. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, 2nd edition, springer series in statistics.

2 Ethem Alpayd-yn, Introduction to machine learning, second edition, MIT press.

3. https://www.analyticsvidhya.com/blog/20 15/04/comprehensive-guide­ data-exploration-sas-using-python-numpy-scipy-matplotlib-pandas/

4. https://www.oreilly.com/library/view/python- for-data/9781491957653/ ch0l.html

Last Updated: Tuesday, January 24, 2023

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