Machine Learning - Monsoon 2017

Instructors Saket Anand and Anubha Gupta
 Lecture Time and Venue Wed, Fri - 10:00AM - 11:30AM; Venue - C11
 Office Hours Saket: Fri - 12:00PM-01:00PM, B-103; Anubha: Wed - 5:00PM-6:00PM, B-106
 TALokender Tiwari (Head TA), Priya Aggarwal, Pravin Nagar, Ananya Harsh Jha, Anant Jain, Anshuman Suri, Kushagra Singh, Divam Gupta, Shrey Bagroy
 TA Office Hours Lokender Tiwari Thursday9:30AM - 10:30 AM, Lab B-207
 Priya Aggarwal
Friday, 2.30PM - 3.30PM, Lab B-207
 Pravin Nagar: Tuesday, 3:00PM - 4:00PM, 4th floor lounge area
 Ananya Harsh Jha: Wednesday, 3:00PM - 4:00PM, CDX, Glass room
 Anshuman Suri: Wednesday, 1:00PM - 2:00PM, Library ground floor. 
 Anant Jain: Tuesday, 11:30AM - 12:30PM, Library ground floor. 
 Shrey Bagroy: Wednesday, 1:30PM - 2:30PM, CDX, Glass room
 Divam Gupta: Tuesday, 11:30AM - 12:30PM, Library Ground Floor
 Kushagra Singh: Friday, 2:30PM - 3:30PM, CDX Glass room
 Course Group Use Backpack for discussions.
 HW Submission  Backpack
 Prerequisites Linear Algebra
 Probability and Statistics
 Advanced Calculus 
 Programming (Python)
 TextbooksReading material will be assigned from different texts along with lecture notes. 
Reference Books:
  1. [TM] Machine Learning. Tom M. Mitchell. McGraw Hill, 1997.
  2. [CB] Pattern Recognition and Machine Learning. Christopher M. Bishop. Springer, 2006.
  3. [SS] Understanding Machine Learning: From Theory to Algorithms. Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press, 2014.
  4. [DHS] Pattern Classification. Richard Duda, Peter Hart and David Stork. Second Ed., Wiley 2006.
  5. [SB] Reinforcement Learning: An Introduction. Richard Sutton and Andrew Barto. MIT Press, 2016 (draft)
The reading material from these books will be referred using the following abbreviation: Chapter 2.3.1 from Bishop's text as [CB-Ch-2.3.1]; Appendix C from Bishop's text as [CB-App-C], and so on.

IIIT-Delhi students can also find these texts here.
 Relevant Journals and Conferences Journals
 JMLRIEEE TPAMIIEEE Trans. on Neural Networks and Learning Systems, Machine Learning
 NIPS, ICML, KDD. Often papers in CVPR, ICCV, AAAI, IJCAI, etc. are also relevant
 Early Submission Policy Most, if not all, assignments will have online submissions, typically at 11:59PM on the assigned date. If you submit your assignment more than 6 hours before the deadline, you will get 5% bonus on that assignment.
 Late Submission PolicyThe deadlines will be hard. Please prepare to submit a couple of hours before the deadline. Network connection problems, hard drive crashing, blue screen of death, and similar reasons will not be honored for missing the deadline. 

You have three days of extension which can be used any time over the semester. The quantum of extension will be one day. There will be no additional extensions whatsoever on grounds of medical emergency etc. 

For project related deadlines, the no. of available extension days will be the minimum of that of the group members. Every time your group uses an extension day for a project related submission, one will be deducted from each group member's quota. 

Late submission would automatically use up one extension day. If none remain, then a zero will be assigned for that submission.
 Graded Sheet Collection  Policy TAs will announce the date and time of collection on backpack. 
 If you are unable to collect your sheet on the specified day, you have the following options. 
1. Request a friend to collect your sheet and inform the TA by email. Without your email, the answer sheet cannot be handed over to your friend. 
2. Within one week of the announced date, you (or your friend) may collect your answer sheets during the TA's office hours (or by appointment). 
3. In case of any discrepancy in grading, you must take the issue up within one week of the announced date. No requests will be entertained after this one week period. 
 Doubt Resolution PolicyFor resolving doubts about concepts, assignments, etc., please visit Instructor/TA during their office hours or with prior appointment (by email). 
Use backpack extensively to resolve doubts regarding assignments. The TAs/Instructors will be responsive on backpack. 
Note: Please make sure you ask your doubts about Assignments at least 2 days before the submission deadline. The TAs will not be held responsible for not answering your queries in the last two days of your query.   
 Plagiarism Policy  Every submission (written and code) will be run through a plagiarism checker. IIITD's academic dishonesty policy will be enforced. 
Course Description Course Description contains the prerequisites, post conditions, weekly plan and other information.  
 Auditing RequirementsStudents need to submit all assignments and projects on time. Quiz and Exams are optional.