The specific topics discussed in CSE 250B will include, not necessarily in this order,
January 7, 2014 |
Maximum
likelihood (ML) |
|
January 9 |
Conditional ML, logistic regression model |
Project 1 due on
January 23 |
January 14 |
Stochastic gradient descent/ascent (SGD) |
Quiz 1 with answer |
January 16 |
Regularization |
|
January 21 |
General log-linear model,
feature functions |
Quiz 2 with answer |
January 23 |
Conditional random fields (CRFs) |
Project 2 due on
February 13 |
January 28 |
Viterbi algorithm for prediction with a CRF |
Quiz 3 with answer |
January 30 |
Partial derivatives for CRFs, forward and
backward vectors |
|
February 4 |
Stochastic gradient and Collins perceptron
for CRF training |
Quiz 4 with answer |
February 6 |
Gibbs sampling, contrastive divergence |
|
February 11 |
Text mining,
bag of words representation, multinomial model |
Quiz 5 with answer |
February 13 |
The latent Dirichlet allocation (LDA)
generative model |
Project 3 due on
February 27 |
February 18 |
Overview of Gibbs sampling for LDA |
Quiz 6 with answer |
February 20 |
Derivation of collapsed Gibbs sampling
formula |
|
February 25 |
Introduction to recursive neural networks |
Quiz 7 with answer |
February 27 |
Due date for Project 2 |
Project 4A or Project 4B, due on Monday March
17 at 10am |
March 4 |
Intro to backpropagation |
Quiz 8 with answer |
March 6 |
Backpropagation in general |
|
March 11 |
Review of backpropagation for scalar nodes |
Quiz 9 with answer |
March 13 |
Review of backpropagation for vector-valued
nodes |
|
March 18 (Tuesday) |
Final
exam from
3pm to 6pm |
You should not drop CSE 250B just because you are unhappy with the score that you receive on a project or quiz. Instead, you should make an appointment to discuss with a TA or the instructor how you can do better on following projects and quizzes.
Most recently
updated on March 13, 2014 by Charles Elkan, elkan@cs.ucsd.edu.