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Dr. Sanjay Chatterji
Ph.D. [IIT Kharagpur]
Dept : Computer Science and Engineering
Email : sanjayc AT
Research Interests:

Machine Learning, Natural Language Processing, Information Retrieval, Data Analytics

Recent Publications:

1. Sanjay Chatterji; Nitish Varshney; Parnab Kumar Chanda; Vibhor Mittal; Bhavi Bhagwan Jagwani, Extracting and Representing Higher Order Predicate Relations between Concepts, 21st International Conference on Applications of Natural Language to Information Systems (NLDB 2016), University of Salford, MediaCityUK Campus, Salford, UK, June 2016, pp 175–186

2. Sanjay Chatterji; Nitish Varshney; Ranjan Kumar Rahul, AspectFrameNet: A FrameNet Extension for Analysis of Sentiments around Product Aspects, Journal of Supercomputing (SCI Indexed), DOI: 10.1007/s11227-016-1808-6, 2016, Springer

3. Sanjay Chatterji; Tanaya Mukherjee Sarkar; Pragati Dhang; Samhita Deb; Sudeshna Sarkar; Jayshree Chakraborty; Anupam Basu, A Dependency Annotation Scheme For Bangla Treebank, Language Resources and Evaluation (SCI Indexed), 48(3), pp 443-477, 2014, Springer Netherlands, DOI 10.1007/s10579-014-9266-3, Print ISSN 1574-020X, Online ISSN 1574-0218.

4. Sanjay Chatterji; Diptesh Chatterjee; Sudeshna Sarkar, An Efficient Technique for De-Noising Sentences using Monolingual Corpus and Synonym Dictionary in Proceedings of COLING 2012: Demonstration Papers, Mumbai, India, December, 2012, pp 59–66

Suggested Links and Downloads

  machine learning :


Course Title: Machine Learning

Paper Code: CS-503

Contact: 3L Credits: 3

Total Lectures: 42

Instructor-In-Charge: Dr. Sanjay Chatterji


Module 1 (10L)

Introduction to Machine Learning, Concept Learning: Find-S, Candidate Elimination, Decision Tree Learning.


Module 2 (10L)

Gradient Descent <link1, link2>, Artificial Neural Networks, Bayesian Learning, Expectation Maximization, K-means Clustering.


Module 3 (10L)

Statistical: Generative and Discriminative approaches (Naive Bayes, Hidden Markov model, Gaussian mixture model, Latent Dirichlet allocation, Conditional random fields, Maximum-entropy Markov models)


Module 4 (12L)

Instance Based Learning: k-Nearest Neighbor, Support Vector machine, Reinforcement Learning, Evaluation Methods, Application in NLP and Graph search



References :

1. Tom M. Mitchell, Machine Learning, 2013 Indian Edition, McGraw-Hill Education, Inc.

2. Machine Learning Course in coursera by Andrew Ng, Link:,

3. Introduction to Machine Learning, Third Edition, Ethem Alpaydin, The MIT Press

4. Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, The MIT Press


Marks Distribution

1. Class Test I: 7.5%

2. Class Test II : 7.5%

3. Two assignments: 5+5 = 10%

3. Attendance : 5%

4. Mid-Semester: 20%

5. End Semester: 50%



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