MACHINE LEARNING WITH PYTHON (18EC745)

MACHINE LEARNING WITH PYTHON

Course Code : 18EC745 
CIE Marks :40
Lecture Hours/Week :03 
SEE Marks :60
Total Number of Lecture Hours : 40 (08 Hrs / Module) 
Exam Hours :03
CREDITS — 03

Course Learning Objectives: This course will enable students to

• Define machine learning and problems relevant to machine learning.
• Differentiate supervised, unsupervised and reinforcement learning
• Apply neural networks, Bayes classifier and k nearest neighbor, for problems appear in machine learning.
• Perform statistical analysis of machine learning techniques.

Module — 1

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 Bookl, Sections: 1.1 — 1.3, 2.1-2.5, 2.7 L1 - L5

Module — 2

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 Bookl, Sections: 3.1-3.7 L1 - L3

Module — 3

Artificial Neural Networks : Introduction, Neural Network representation, Appropriate problems, Perceptrons, Back propagation algorithm. Example program in Python Text book 1, Sections: 4.1 —4.6 L1 - L3

Module —4

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 L1 - L4

Module—5

Evaluating Hypothesis: Motivation, Estimating hypothesis accuracy, Basics of the sampling theorem, General approach for deriving confidence intervals, Difference in error of two hypotheses, 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 Learning Example program in Python. Textbook 1, Sections: 5.1-5.6, 8.1-8.5, 13.1-13.3 L1 - L3

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 Alpaydyn, Introduction to machine learning, second edition, MIT press.
3. https://Www.analyticsvidhya.com/blog/2015/04/comprehensive-guide- data-exploration-sas-using-python-numpy—scipy—matplotlib-pandas/
4. https://www.oreilly. com/library/vieW/python-for-data/978 149 1 95765 3/ ch01 .html