For the version of CSE 255 taught in Winter 2015 by Prof.
Julian McCauley, see http://cseweb.ucsd.edu/~jmcauley/cse255/.
date |
topics |
lecture notes |
handouts |
quiz |
assignment |
April 2 |
Course outline, supervised
learning, overfitting |
Chapters 1, 2 |
FT article |
Sample on page 11 |
p. 22 |
April 9 |
Linear regression, preprocessing, missing
data, regularization |
Chapters 3, 6 |
Quiz 1 with solution |
p. 29 |
|
April 16 |
Linear and nonlinear support
vector machines |
Chapter 5 |
Quiz 2 with solution |
p. 48 |
|
April 23 |
Learning when one class is rare, F1 and AUC
scores |
Chapter 7 |
Quiz 3 with solution |
p. 65 |
|
April 30 |
Estimating calibrated probabilities, making
cost-sensitive decisions |
Chapters 8, 9 |
Quiz 4 with solution |
p. 87 |
|
May 7 |
Sample selection bias, importance weighting,
reject inference |
Chapter 10 |
Quiz 5 with solution |
p. 99 |
|
May 14 |
Recommender systems, collaborative filtering,
matrix factorization via alternating least squares |
Chapter 11 |
Quiz 6 with solution |
p. 111 |
|
May 21 |
Text mining: bag of words representation,
classifier learning |
Chapter 12 |
Quiz 7 with solution |
p. 125 |
|
May 28 |
Network analytics, link prediction, singular
value decomposition (SVD) |
Chapter 14 and Section 13.1 |
Quiz 8 with solution |
p. 145 |
|
June 4 |
Guest lecture by Dr. Ramon
Huerta |
Quiz 9 with solution |
|||
June 11 |
Final
exam at 7pm |
The instructor is Charles Elkan (Professor), whose office is in the CSE building, room 4134. Feel free to send email to arrange an appointment. The teaching assistant is Eric Christiansen. He will have office hours in room B250A in the basement of the CSE building three days per week: at 5:30pm on Tuesdays, 2:30pm on Thursdays, and 2pm on Fridays.
Each week there
will be a hands-on assignment due in class the next week. Assignments
will include pointers to datasets. You should do each assignment
in a team of exactly two people. You are free to keep the same
partner for multiple assignments, or to switch. You should look
for intellectual diversity in whom you choose as a partner.
Specifically, people from the same company should not work
together. Students from outside CSE should pick partners who are
CSE students, or similar. In each pair, at least one student
should be good at writing code in multiple programming
languages.
For each
assignment, each team should turn in a brief joint report. Each
report should be single-spaced and include figures, tables, and
citations as appropriate. Do not include appendices or listings
of code. Grades will be based entirely on the joint reports.
Reports should be concise, more like memos than like full papers.
Reports will be graded using this rubric.
Most recently updated on June 6, 2013 by Charles Elkan, elkan@cs.ucsd.edu