(Applicable From The Academic Session 2018-2019) : Syllabus For B. Tech in Computer Science & Engineering
(Applicable From The Academic Session 2018-2019) : Syllabus For B. Tech in Computer Science & Engineering
(Applicable From The Academic Session 2018-2019) : Syllabus For B. Tech in Computer Science & Engineering
2. Programming Multi-agent Systems in Agent Speak Using Jason. Rafael H. Bordini, Jomi
Fred Hubner and Michael Wooldridge (Wiley, 2007)
Machine Learning
Code: PEC-CS701E
Contacts: 3L
COURSE OBJECTIVE
To learn the concept of how to learn patterns and concepts from data without being
explicitly programmed
To design and analyse various machine learning algorithms and techniques with a modern
outlook focusing on recent advances.
Explore supervised and unsupervised learning paradigms of machine learning.
To explore Deep learning technique and various feature extraction strategies.
Hrs/unit Marks/unit
Unit 1: 10
Supervised Learning (Regression/Classification)
Basic methods: Distance-based methods, Nearest-Neighbours, Decision
Trees, Naive Bayes
Linear models: Linear Regression, Logistic Regression, Generalized
Linear Models
Support Vector Machines, Nonlinearity and Kernel Methods
Beyond Binary Classification: Multi-class/Structured Outputs, Ranking
8
PG
Maulana Abul Kalam Azad University of Technology, West Bengal
(Formerly West Bengal University of Technology)
Syllabus for B. Tech in Computer Science & Engineering
(Applicable from the academic session 2018-2019)
Unit 2: 7
Unsupervised Learning
Clustering: K-means/Kernel K-means
Dimensionality Reduction: PCA and kernel PCA
Matrix Factorization and Matrix Completion
Generative Models (mixture models and latent factor models)
Unit 3 6
Evaluating Machine Learning algorithms and Model Selection, Introduction to
Statistical Learning Theory, Ensemble Methods (Boosting, Bagging, Random
Forests)
Unit 4 9
Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep
Learning and Feature Representation Learning
Unit 5 9
Scalable Machine Learning (Online and Distributed Learning)
A selection from some other advanced topics, e.g., Semi-supervised Learning,
Active Learning, Reinforcement Learning, Inference in Graphical Models,
Introduction to Bayesian Learning and Inference
Unit 6: 5
Recent trends in various learning techniques of machine learning and
classification methods
References:
1. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical
Learning, Springer 2009 (freely available online)
3. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007
4. Dr. Rajiv Chopra, Machine Learning, Khanna Publishing House, 2018