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3CP10

This document outlines a machine learning course that introduces basic concepts and techniques. It is a 4-credit course consisting of 3 hours of lectures and 2 hours of practical work per week. The course covers topics like linear models, artificial neural networks, clustering, deep learning, and reinforcement learning. Students will learn machine learning techniques and be able to apply them to solve real-world problems. Assessment includes exams, assignments, and a practical component.

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85 views1 page

3CP10

This document outlines a machine learning course that introduces basic concepts and techniques. It is a 4-credit course consisting of 3 hours of lectures and 2 hours of practical work per week. The course covers topics like linear models, artificial neural networks, clustering, deep learning, and reinforcement learning. Students will learn machine learning techniques and be able to apply them to solve real-world problems. Assessment includes exams, assignments, and a practical component.

Uploaded by

bharat
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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3CP10: MACHINE LEARNING

CREDITS - 4 (LTP: 3,0,1)

Course objective:
To introduce basic concepts and techniques of machine learning.

Teaching and Assessment Scheme:


Teaching Scheme
Credits Assessment Scheme Total
(Hours per week)
Marks
Theory Marks Practical Marks
L T P C
ESE CE ESE CE
150
3 0 2 4 60 40 20 30

Course Contents:
Unit Topic Teaching
No. Hours
1 Introduction 05
Probability theory, Naive Bayesian and Linear Regression, Overview of Machine
Learning and its Applications
2 Linear Models for Classification 08
Discriminant functions, Probabilistic generative models, Probabilistic
discriminative models, Support Vector Machine (SVM), Applications.
3 Artificial Neural Networks 10
Introduction, Neural Network representation, Perceptron, Activation Functions,
Feed Forward Network and Recurrent Network, Multilayer network and back
propagation, Learning with momentum, Error minimization, Applications.
4 Clustering 10
Unsupervised learning, Clustering methods, Method based on Euclidean
distance and other similarity measures, Partitioning, Hierarchical, Density based
approach, Decision Tree, Method based on probabilities, K – means algorithm,
Self-organizing map
5 Deep Learning 08
Introduction, Different models, Convolution Neural Network (CNN), RNN,
LSTM, Introduction to Tensorflow
6 Reinforcement Learning 04
Introduction, the learning task, Q learning, non-deterministic rewards and
actions, Temporal difference learning.
Total 45

List of References:
1. Tom Mitchell, “Machine Learning”, McGraw Hill
2. C. Bishop, “Pattern Recognition and Machine Learning”, Springer
3. Nils J Nilsson , “Introduction to Machine Learning”, Stanford University
4. Smola and Vishwanathan, “Introduction to Machine Learning “, Cambridge University Press
5. Stuart Russell, Peter Norvig, "Artificial Intelligence: A Modern Approach", Pearson Education

Course Outcomes (COs):


At the end of this course students will be able to …
1. Illustrate the basic concepts of machine learning.
2. Compare and identify suitability of supervised and unsupervised learning techniques
3. Apply various machine learning techniques to solve real world problems
4. Describe deep learning and reinforcement learning techniques
5. Develop applications using appropriate machine learning technique

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