Machine Learning (CS60050)

Instructor: Sourangshu Bhattacharya

Teaching Assistants: Shubhadip Nag, Saransh Sharma, Bismay Parija, Saurabh Roy, Kale Chaitanya Hanumant Kanchan

Class Schedule: Monday (8:00 - 9:55), Tuesday (12:00 - 12:55)

Classroom: NR-412 (Nalanda Complex)

Announcements:

Moodle course has been created. Name: CS60021: Machine Learning (Spring 2025)., URL: https://moodlecse.iitkgp.ac.in/moodle/. Student registration key is shared in email.
First asssignment given on moodle. Deadline: 25th January.
First class test will be held on 21st January. Syllabus till linear model.
Second asssignment given on moodle. Deadline: 15th February.
Second class test will be held on 11th February. Syllabus will be till Ensemble Models.
Third asssignment given on moodle. Deadline: 22nd March.
Third class test will be held on 18th March. Syllabus will be till Graphical Models.

Thomas M. Mitchell, "Machine Learning." McGraw Hill (1997). -- Traditional book for concept learning.
Christopher M. Bishop, "Pattern Recognition and Machine Learning." Springer (2006). Download Link -- Main book for most models.
Raschka, Sebastian, Yuxi Hayden Liu, and Vahid Mirjalili.
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python.
Packt Publishing Ltd, 2022. -- Practice Programming with github code link

Week	Dates	Topic / Activity	Links / Material
Week 1	6/1, 7/1	Introduction to ML and concept learning	Slides
Week 2	13/1, 14/7	Linear models, regression, classification	Slides
Week 3	20/1, 21/1	Support Vector Machines	Slides
Week 4	27/1, 28/1	Probabilistic model for ML	Slides
Week 5	3/2, 4/2	Decision Trees, Ensembles, Random Forests	Slides
Week 6	10/2, 11,2	Boosting, Xgboost, Crossvalidation	Slides - Boosting, Slides - Crossvalidation
Week 7	3/3, 4/3	Clustering - K-means, GMM, Hierarchical, Spectral	Slides - Clustering
Week 8	10/3, 11/3	Graphical Models	Slides - Graphical Models
Week 9 + 10	17/3, 18/3, 24/3, 25/3	Neural Networks, Convolutional Neural Networks, Applications in Computer Vision	Slides - Neural Networks
Week 11	1/4, 7/4, 8/4	Recurrent Neural Networks, Transformers	Slides - RNN, Transformers

Introduction and concept learning, Linear models for classification and regression, SVMs and kernel methods
Probabilistic ML, Naive Bayes, Decision Trees, Bagging, Random forests
Boosting, Xgboost
Clustering, Gaussian Mixture Models
Graphical Models
Neural Networks
Learning Theory and Metrics for problems
Active, Transfer, multi-task learning
Explainability and Trustworthiness