Instructor: Virgil Pavlu v.pavlu@northeastern.edu but I prefer Teams-chat
Meetings:
Online (Teams) Wed + Fri, 6pm-7:30pm. Lectures are NOT MANDATORY, and will be recorded for later viewing. HWs can be done with the materials on the website as published, and help from TAs at OH
Please use the discussion forum for all questions regarding material, assignments, due dates, data issues, programming issues, etc. That is, do not use the direct email to TAs or Instructors for these questions.
Personal/private matters, such as availability, delays, grades, term projects or other advanced material, etc, should be discussed by email.
[CB] Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, and David G. Stork
[KMPP] Kevin Murphy : Machine
Learning, A Probabilistic Perspective
[HTP] Hastie, Tibshirani: An Introduction to Statistical Learning: with Applications in Python
Other texts
Pattern Recognition and Machine Learning by C. Bishop
Introduction in Machine Learning by Ethem Alpaydin
Machine Learning by Tom Mitchell
http://www.stanford.edu/class/cs229/section/cs229-linalg.pdf
http://www.stanford.edu/class/cs229/section/cs229-prob.pdf
http://www.stanford.edu/class/cs229/section/cs229-cvxopt.pdf
http://www.stanford.edu/class/cs229/section/cs229-cvxopt2.pdf
There will be about seven assignments (one
per module, every two weeks), including programming ; please
see the syllabus for an exact schedule. Some of the problems
can be difficult, and it will often be helpful to discuss
them with others. Feel free to form study groups. However,
the idea is for everyone to understand the problems and
experience working through the solutions, so you may not simply "give" a solution to another classmate. In
particular, each student must
write up his or her own homework
solutions/code and must
not read or copy the
solutions/code of others. If you work with others on a
problem, you must
note with whom you discussed the
problem at the beginning of your solution write-up.
Late homework policy: Homework is due
on the announced due
date. You will be granted one homework extension of 7 days
total, to be used at your discretion, no questions asked. This policy does not apply to projects. After
the first 7 late days, unexcused late assignments will be
penalized 10% per calendar
day late, but no more
than 60% of the total (i.e. you can still get 40% of the
points by submitting before the term ends)
Optionally students can work on a project, if they discuss it in advance with the instructor .
We plan to have a final exam, 3 hours. The exam will be on programing exercises (students will use their laptops), no paper submission.
All work submitted for credit must be your own.
You may discuss the homework problems or projects with your classmates, the teaching assistant(s), and instructor. You must acknowledge the people with whom you discussed your work, and you must write up your own solutions. Any written sources used (apart from the text) must also be acknowledged; however, you may not consult any solutions from previous years' assignments whether they are student or faculty generated.