Machine Learning

CSCI 567, Fall 2018

Haipeng Luo


General Information  |  Schedule & Readings  |  Homework & Exams

*Schedule is subject to small adjustments.
*Slides will be posted. They are largely based on those from Fall 2017 by Prof. Fei Sha.

Date Topics Recommended Reading
08/22 Course overview; ML overview; nearest neighbor classification;
core ML concepts; typical steps of developing a ML system
slides handouts
08/29 Linear regression; regression with nonlinear basis; regularized regression slides handouts
[MLaPP] 1.4.5, 7.1-7.3, 7.5.1, 7.5.2, 7.5.4, 7.6, 1.4.7, 1.4.8
[ESL] 7.1, 7.2, 7.3, 7.10
09/05 Linear discriminant analysis; Perceptron; logistic regression slides handouts
[MLaPP] 4.2.1 - 4.2.5, 8.5.1-8.5.4, 1.4.6, 8.1-8.3
[ESL] 4.1-4.2, 4.4
09/12 Multiclass classification; neural networks slides handouts
[MLaPP] 16.5.1-16.5.6, 28
[ESL] 11.3-11.7
09/19 Convolutional neural nets; kernel methods handouts
[MLaPP] 14.1, 14.2.1-14.2.4, 14.4.1, 14.4.3
[ESL] 5.8, 6.3, 6.7
09/26 Lagrangian duality; SVM slides handouts
[MLaPP] 14.5.2-14.5.4
[ESL] 12.1-12.3
10/03 First exam
10/10 Decision trees; boosting slides handouts
[MLaPP] 16.4.1-16.4.5, 16.4.8, 16.4.9
[ESL] 16.3
10/17 Midterm review; clustering; Gaussian mixture models slides handouts EM_demo
[MLaPP] 3.5, 11.1-11.3, 11.4.1-11.4.4, 11.5
[ESL] 6.6.3, 8.5, 14.3.1-14.3.9,
10/24 Density estimation; generative models; naive Bayes slides handouts
[MLaPP] 17.1-17.4, 17.5.1-17.5.2
10/31 Hidden Markov models; dimensionality reduction and visualization; PCA slides handouts
[MLaPP] 10.1, 10.2.1-10.2.3, 10.3-10.5, 12.2
[ESL] 14.5.1
11/07 Multi-armed bandit; reinforcement learning slides handouts
11/14 Guest lecture by Dr. Bilal Shaw on "fraud detection in real world"
11/21 No class -- Thanksgiving
11/28 Second exam