Homework 6
Due: 03/03/2015 (before class)
February 23, 2015
Problem 1 (20 pt) Regularized Logistic Regression
Let D = cfw_(x1 , y1 ), . . . , (xn , yn ) be the training examples, where xi 2 Rd and yi
Homework 5
Due: 02/24/2015 (before class)
February 15, 2015
Problem 1 (20pt) Naive Bayes Classifier
In this homework, you are asked to implement a Naive Bayes classifier for text categorization. In pa
Homework 8
Due: April 9, 2015 (before class)
March 31, 2015
Problem 1 (15pt) Hedge Algorithm
In class, we discussed the Hedge algorithm, which learns positive weights to combine the predictions from m
Homework 7
Due 03/31/2015 (before class)
March 23, 2015
Problem 1 (20pt): Train and Test Support Vector Machine
Download the SVM software from the website http:/svmlight.joachims.org/. Read the docume
Homework 4
Due: 02/17/2015 (before class)
February 9, 2015
Problem 1 (20pt): Bayesian model selection
In this homework, you are asked to compute the result of Bayesian model selection for linear regre
Homework II
Due: 01/29/2015 (before class)
January 22, 2015
Problem 1 (10pt): Noise Model
In the class, we assume the following data generative model
t = y(x, w) +
where N (|0,
distribution, i.e.,
1
Semi-supervised Learning
Rong Jin
Spectrum of Learning Problems
What is Semi-supervised Learning
Learning from a mixture of labeled and unlabeled examples
Labeled Data
Unlabeled Data
L = f (xl1; y1);
Information Filtering
Rong Jin
1
Outline
Brief introduction to information filtering
Collaborative filtering
Adaptive filtering
2
Short vs. Long Term Info. Need
Short-term information need (Ad hoc ret
Homework 3
Due: 02/10/2015 (before class)
February 1, 2015
Problem 1 (20pt): Experiment with Lasso Regularization
Data set A data set is provided in the file diabetes.mat that can be downloaded from h