CS 419: Introduction to Machine Learning (Autumn 2018)
Lecture Schedule Slot 5, Wednesday, Friday: 9:30--10:55.
Instructor: Sunita Sarawagi
Rasna Goyal, Avinash Modi, Himadrishekhar Bandyopadhyay, Anupama Vijjapu, Kalyani Vishwakarma, Karan Taneja
Instructor's office hours check here
Syllabus and week-wise calendar: Click here
Homeworks Click here
Questions Post questions only meant for instructors and TAs to firstname.lastname@example.org. Post questions of general interest to moodle.
This course is part of the Computer Science minor for non-CSE
undergraduates. Eligibility is as per institute norms for minors.
Do not send me email about CS419 registration asking for overriding the norms set by the institute.
This course will provide a broad overview of Machine Learning
We will go over core machine learning tools and cover their usage in example applications. Topics include:
Supervised learning methods like Decision trees, Generative classifiers like naive Bayes, Support vector Machines, Logistic classifiers, Neural Networks.
Unsupervised learning like K-Means clustering, EM.
The hard pre-requisite for the course is CS101. But a basic background of probability and statistics and linear algebra will be assumed. A course like Data Analysis and Interpretation (IC 102) or equivalent as taught by the respective departments is strongly recommended. The upper limit on class strength is 150. Also, an introductory course on Data Structures and Algorithms (e.g. CS 213) is recommended. The homework assignments will require you to write programs in C++ or Python. If you are not familiar with these tools, you might feel challenged.
Approximate credit structure (Subject to change)
- 25% Mid-semester exam
- 40% End semester exam
- 20% Homeworks
- 15% In class quizzes (mostly surprise)
Understanding Machine Learning. Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press. 2017. Available online.
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006. website
Hastie, Tibshirani, Friedman
The elements of Statistical Learning
T. Mitchell. Machine Learning. McGraw-Hill, 1997.
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
Boyd and Vandenberghe
Convex optimization Book available online:
by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.