Machine Learning

CSCI 567, Fall 2018

Haipeng Luo

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