CSL 603 - Machine Learning- Fall 2017


Course Information Grading Policy Lectures/Calendar Labs


Course Information


Timings and Lecture Hall

Monday - 11.45am-12.35pm
Tuesday - 9.00-9.50am
Wednesday - 9.55-10.45am

Transit Campus II - Lecture Room H8

Lab hours: Friday 9.00am-10.35am



Machine Learning (ML) is the study of computer algorthms that learn and imrpove automatically through experience. ML is an increasingly popular subject due to a wide variety of applications such as autonomous vehicles, hand-written character recognition, automatic speech processing, recommendation systems, etc. This introductory (undergraduate-graduate bridge) course discusses some of the basic and widely used ML techniques, covering a wide range of topics such as supervised and unsupervised learning, classification and regression, artificial neural networks, and dimensionality reduction. For a comprehensive list of topics covered in the course and course schedule, please see the course calendar. Practical experience will be gained through implementing the ML algorithms for different applications in Python/C/C++. For more details on lab assignments, please see the Labs webpage. This course has a pre-requisite of CSL201 (Data Structures). Background in Linear Algebra, Probability and Statistics, and Optimization will be helpful, though not necessary.


Reference Material

There is no fixed textbook for the course. However content will be adopted from the following textbooks


Instructor Details

Narayanan (CK) Chatapuram Krishnan

Office Hours: 11.00am-12.00pm

Office: 318

Phone: +91 1881 242273

Email: ckn@iitrpr.ac.in


Teaching Assistants Details

Sanatan Sukhija

Office: 120

Office hours: Wednesday all day (with prior appointment)

Email: sanatan@iitrpr.ac.in

Akanksha Paul

Office: 237

Office hours: Monday after class

Email: akanksha.paul@iitrpr.ac.in


Academic Integrity

It is expected that students who are taking this course will demonstrate a keen interest in learning and not mere fulfilling the requirement towards their degree. Discussions that help the student understand a concept or a problem is encouraged. However, each student must turn in original work. Plagiarism/copying of any form, will be dealt with strict disciplinary action. This involves, copying from the internet, textbooks and any other material for which you do not own the copyright. Copying part of the code will be considered plagiarism. Lending the code to others will be considered plagiarism too, for it is difficult to investigate who copied whose code. Students who violate this policy will directly receive a failing grade in the course. Remember - Your partial submission can fetch you some points, but submitting other's work as your own can result in you failing the course. Please talk to the instructor if you have questions about this policy. All academic integrity issues will be handled in accordance with institute regulations.


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Grading Policy


Grading Policy

Quizzes: There will be approximately 4 pre-announced quizzes during the semester. Check the course calendar to learn about dates on which a quiz will be held. All the quiz scores will count towards the student's overall grade. The quizzes will account for 20% of the overall grade.


Labs: There will be 4 labs. Each lab will have a major programming component and will span for approximately two-three weeks. All the 4 labs will account for 20% of the overall grade. Students having difficulty with the labs are encouraged to contact the TA for assistance. You are not required to be physically present in the lab during the lab hours. You can complete the labs at your convenience and turn it in by the deadline. There will be penalty for late submission of the labs. It will start at 1% for the first hour after the submission deadline and increase exponentially for every hour hence forth.


Project: There will be a course project worth 10% of the overall grade. This will be a group project. The details of the project will be announced as the semester progresses.


Exams: The mid and end semester exams together will account for 50% (25% each) of the overall grade.


Attendance: There is no mandatory attendance. However attendance will be taken in every class. This will consitute a bonus of 1% for the final grade and might be helpful for all border line students.


Passing Critera: A student must secure an overall score of 40 (out of 100) and a combined score of 60 (out of 200) in the exams to pass the course.


Tentative Grade Breakup*

Quizzes (4) 20%
Labs (4) 20%
Project 10%
Mid-Semester Exam 25%
End-Semester Exam 25%
Total 100

*This is a tentative breakup of the grades and can change at the discretion of the instructor. However, any change with respect to the grade break-up will be intimated in advance.


Grade Sheet:PDF

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Lectures and Calendar


Tentative Schedule and List of Topics*

Week Topic and Readings
1 (Aug7-Aug11) Introduction and Supervised Learning
  • Chapter 1 (ML)

  • Chapter 1 and 2(IML)

Scribes: Aditya Ranjan, Manish Kumar, Pratham Gupta, Rohit
2 (Aug14-Aug18) Decision Tree Learning
  • Chapter 3 (ML)

Other Reference Material: Decision Forests for Classification by Criminisi et al. 2011 (refer Chapters 1-3)
Scribes: Katta Sai Srinadhu, Sarthak Gupta, Vinit Jagdish Kothawade
3 (Aug21-Aug25) Linear Regression
  • Notes by Andrew Ng (Part I - 1-5)

  • Chapter 3 (ESL) (for Regularization and LASSO)

  • Chapter 3 - 3.1-3.2 (PRML) (for Bias-Variance Analysis)

Other Reference Material: Matrix and Vector Algebra Review
Scribes: Allu Krishna Sai Teja, Katyayani Jaiswal, Koustav Das, Vishal Singh
4 (Aug28-Sep1) Linear Classification
  • Chapter 4 - 4.1-4.3 (ESL) (for Classification through Linear Regression, Linear Discriminants and Logistic Regression)

  • Notes by Andrew Ng (Part II) (for Logistic Regression and Netwon Raphson Method)

Other Reference Material: Optimizers for Logistic Regression
Scribes: Eswar Dev Harsha, Gaurav Kumar, Rajat Sharma, Thota Venkata Sainath
5 (Sep4-Sep8) Artificial Neural Networks
Readings: Other Reference Material: Scribes: (week 5) Alok Kiran, B Yugandhar, Eeshan Sharma, Narotam Singh
Scribes: (week 6) Love Mehta, Nikhil Kumar, Nittin Singh, Sujit Rai
6 (Sep11-Sep15)
7 (Sep18-Sep22) Experimental Design
  • Chapter 4 (ML) (for interval estimation and hypothesis testing)
  • Chapter 19.6-19.11 (IML) (for measures of performance, design of experiments, and hypothesis testing)
Other Reference Material:
Scribes: Anirudh Sharma, Chaudhari Milan Jayeshbhai, Harshita Modi, Kartik Vishwakarma
8 (Sep25-Sep29) Reserve Week
Other Reference Material:
Scribes: Jatin Goyal, Pratibha Kumari, Sachin Bijalwan, Shreya Dubey
9 (Oct2-Oct6) Exam Week Mid-Sem Mid-Sem-Sol
10 (Oct9-Oct13) Bayesian Learning
  • Chapter 6 (ML)
Other Reference Material:
Scribes: Aditya Gupta, Mandhatya Singh, N Nikhil, Sagarika Sharma
11 (Oct16-Oct20) Hidden Markov Models
Readings: Other Reference Material:
  • Chapter 13.1-13.2 (PRML)
Scribes: Piyush Jain, Shubham Dham, Sonu, Unit Bhupendra Patel
12 (Oct23-Oct27) Kernel Methods
  • Chapter 13 (IML)
  • Notes by Andrew Ng
  • SMO by Platt
Other Reference Material:
  • Lagrange Multipliers Appendix E (PRML)
  • Lectures 12.1-12.6 of Andrew Ng on coursera (to get a quick overview)
  • LibSVM - popular SVM toolbox
Scribes: (week 12) Keshav Garg, Pankaj Verma, Prateek Munjal, Shivam Mittal
Scribes: (week 13) Pratik Chhajer, Manas Gupta, Soumyadeep Roy
13 (Oct30-Nov3)
14 (Nov6-Nov10) Clustering
  • Chapter 14 - 14.3 (ESL) (for k-Means, Hierarchical Clustering)
  • Chapter 9 - 9.1-9.2 (PRML) (for Gaussian Mixture Models)
Other Reference Material: Scribes: Ankit Kumar Meena, Bishal Gosh, Naman Goyal, Shikar Jaiswal
15 (Nov13-Nov17) Dimensionality Reduction
Readings: Other Reference Material:
  • Kernel PCA (notes)
  • LDA - Chapter 5.7-5.8 - Pattern Classification by Duda, Hart and Stork
  • Non-linear dimensionality reduction ISOMAP
Scribes: Himanshu Dahiya, Krishan Dev, SD Mahanoor, Sumit Singh

16 (Nov20-Nov24) Reserve Week
Other Reference Material:
Scribes: Abhash yadav, Abhishek Chowdhry, Manish Singh
17 (Nov27-Dec1) Exam Week End-Sem End-Sem-Sol

*This is a tentative list of topics that will be covered during the semester. The topics and schedule can change according to the need at the discretion of the instructor.

ML - Machine Learning

PRML - Pattern Recognition and Machine Learning

IML - Introduction to Machine Learning

ESL - Elements of Statistical Learning

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