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
costsensitive 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 handson 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 singlespaced 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