CS 534
|
|
Date |
Topic |
Recommended |
Notes |
| April 2 |
Introduction, basic concepts |
Chapter 1 Probability Theory Review: A.4 and Andrew Ng's review |
PDF |
| April 4 |
Perceptron algorithm | Section 5.1,2,4,5 | PDF |
| April 6 |
Logistic regression | class notes |
PDF |
| April 9 |
Linear
Discriminant
Analysis (LDA) |
Section 2.1-6 | PDF |
| April 11 |
cont. LDA. |
||
| April 13 |
Off-the-Shelf Classifiers, Decision
Trees (DT) |
Pattern
Classification Chapter 8 or Macine Learning Chapter 3 |
|
| April 16 |
cont. DT, Neural Networks (NNET) |
Pattern
Classification Chapter 6 |
PDF |
| April 18 |
cont. NNET, Nearest Neighbor
Classifiers (NNC) |
Optional Reading: Machine Learning Chapter 8 |
PDF |
| April 20 |
cont. NNC, Support Vector Machines
(SVMs) |
Optional Reading Pattern Classification Section 5.2; C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998 |
PDF |
| April 23 |
cont. SVMs |
PDF |
|
| April 25 |
Naive Bayes Classifiers |
Optional Reading: Generative and Discriminative Classifiers:Naive Bayes and Logistic Regression , chapter by Tom Mitchell for the 2nd edition of his Machine Learning book |
PDF |
| April 27 |
Computational Learning Theory (COLT): PAC Learning |
Optional:
Machine Learning Chapter
7 |
PDF |
| April 30 |
COLT: Inconsistent Hypotheses and
VC-Dimension Bounds |
Optional: Machine Learning Chapter 7 |
PDF |
| May 2 |
cont. COLT, Model Selection and
Regularization |
Pattern Classification
Chapter 9 Machine Learning Chapter 6 (6.1-6.6) |
PDF |
| May 4 |
Midterm Review , cont. Model Selection and Regularization | ||
| May 7 |
Ensemble Learning: Bagging, Equivalence of Weak and Strong Learnability | Pattern Classification Chapter 9 (9.4-5) | |
| May 9 |
AdaBoost, Review Final Project | Pattern Classification Chapter 9 (9.4-5) | |
| May 11 |
Midterm (Different Location: BEXL 412) | ||
| May 14 |
Functional Gradient Boosting |
"Greedy
Function Approximation: A Gradient Boosting Machine", Jerome
Friedman (required Sections 1,2,3, 4.4) |
|
| May 16 |
cont. Gradient Boosting, Sequential
Supervised Learning (SSL) |
||
| May 18 |
cont. SSLw/ Hidden Markov Models |
Pattern Classification 3.10 (through 3.10.5) | |
| May 21 |
cont. SSL w/ HMMs |
||
| May 23 |
SSL w/ Perceptron |
Supplemental
Reading: "Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms" Michael Collins, EMNLP 2002 |
|
| May 25 |
SSL w/ Conditional Random Fields |
Supplemental Reading: "Conditional Random Fields: An Introduction", Hanna Walsh, Technical Report MS-CIS-04-21. Dept. of Computer and Information Science, University of Pennsylvania, 2004. |
|
| May 28 |
Memorial Day: No Class |
||
| May 30 |
Unsupervised Learning: Hierarchical
Agglomerative Clustering |
Pattern Classification 10.9 |
|
| June 1 |
K-means clustering, Probabilistic
Clustering |
Optional Reading: Pattern Classification 10.4.1-10.4.3 |
|
| June 4 |
Expectation Maximization,
Dimensionality Reduction |
||
| June 6 |
Pricipal Component Analysis,
Semi-Supervised Learning |
||
| June 8 |
Finish Semi-Supervised Learning, Review, Q/A |
||
| June 14 |
Final Examination: 9:30a-11:30a |
.