Coordinates 
T/Th 121:20pm, WNGR 149
[Registrar]
[Canvas]

Instructor 
Liang Huang

TAs 
Dezhong Deng
Yilin Yang

Office Hours (tentative) 
LH: TBD, KEC 2069
TAs: Dezhong T/Th 1011am, Yilin W/F 45pm, both at KEC Atrium.
Additional office hours available before exams.

Prerequisites

 CS: algorithms and datastructures. fluent in at least one mainstream languages (Python, C/C++, Java).
HWs will be done in Python+numpy only.
 Math: linear algebra, calculus, and basic probability theory. good sense of geometric intuitions.

Textbooks

 Hal Daume III. A Course in Machine Learning (CIML). default reference. easy to understand.
 Tom Mitchell (1994). Machine Learning. a classical textbook.
CS perspective.
an easy read.
outdated but still more helpful than most recent ones.
 Mohri et al (2012). Foundations of Machine Learning. theory perspective. covers more recent advances such as SVMs that weren't covered in Mitchell.
 Bishop (2007). Pattern Recognition and Machine Learning (PRML). Actually I do not recommend it, definitely not for beginners. But the figures are pretty and I use them in my slides.

Grading

 Midterm: 25%. NO FINAL EXAM.
 Project (groups of up to 3): 25%. No late submission is allowed.
(5% proposal, 5% presentation, 15% report).
 HWs (programming, groups of up to 3): 10% x 3 = 30%.
 EXs (theoretical, individual): 3% x 2 = 6%.
 Class Participation: 6%.
 Quiz (tentatively before thanksgiving): 8%.
 Late Penalty: Each student can be late by 24 hours only once without penalty.
No more late submissions will be accepted. If a group submission is late,
it is considered late for all teammates.
E.g., if a team of A and B submits late and it's the first late submission from A
and the second from B,
then A will receive credit for this submission but B will not.
