M. Sc., COMPUTER SCIENCE (2022 – 2024 Batch)
Semester III
Paper Name: MACHINE LEARNING
Paper
Code: 22PCSCC32
UNIT I
Introduction:
Basic definitions – Learning - Machine Learning vs AI - Machine Learning – features – samples – labels -
Real-world applications and problems – hypothesis test -approaches of machine
learning model - Data pre-processing.
UNIT II
Representation
of formal ML model: The statistical learning framework – training - testing – validation - cross validation - parametric and
non-parametric methods - Difference between Parametric and Non-Parametric
Methods and examples.
UNIT III
Supervised
learning Algorithms: Introduction – Approaches for classification – Linear Regression - Logistic regression - Decision Tree classification algorithm –
Tree pruning - Rule based Classification –IF- THEN
rules classification - Naïve Bayesian classification - Neural Network: Introduction to ANN – Feed Forward Neural Network – Back propagation –.Support
Vector Machines (SVM) - Lazy learners: k- Nearest
Neighbor (k-NN) Algorithm – Case Based
Reasoning (CBR) - Random Forest algorithm.
UNIT IV
Unsupervised
learning algorithms: Introduction– Defining Unsupervised learning – Cluster Analysis – Distance measures - Types of
Clustering – Partition algorithms of clustering –Hierarchical clustering
algorithms - Density based methods.
UNIT V
Reinforcement
Learning and ELM: Introduction: Markov Decision process - Monte Carlo
Prediction - case studies – Applications. Introduction to Extreme Learning
Machine (ELM) - Deep learning
fundamentals: Convolutional Neural Networks (CNN) - Deep Belief Networks (DBN). Software Tools: Introduction to
Weka, Matlab, Rapidminer, Tensorflow and Keras – case studies.
Text Books
1. Anuradha Srinivasaraghavan, Vincy Joseph
(2019), Machine Learning, Wiley.
2. Balas Kausik Natarajan (1991), “Machine Learning: A Theoretical Approach”,
Morgan Kaufmann
3. Dinesh Kumar U Manaranjan Pradhan (2019), Machine learning using Python, Wiley.
4. Etham Alpaydin (2015), Introduction to Machine Learning, third
edition, PHI Learning Pvt. Ltd.
5. Jiawei Han, Micheline Kamber, Jian Pei
(2012), Data mining concepts and
techniques, Morgan Kaufmann Publishers, Elsevier.
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