CSCI 680-1 Machine Learning

General Information


--------------------------

Schedule:

8/27 (Tu) Class begins on 8/28
8/29 (Th)
introduction, linear regression, gradient descent
Week 1

9/3 (Tu)
linear regression in closed form, probabilistic interpretation
9/5 (Th)
logistic regression, softmax regression, perceptron
Week 2


9/10 (Tu)
Newton's method, least mean squares with the kernel trick
9/12 (Th)
SVM, primal and dual optimization
Week 3


9/17 (Tu)
xxx
9/19 (Th)
kernel method, SVM
Week 4


9/24 (Tu)
Kernel, regularization, SMO (sequential minimal optimization), coordinate ascent
9/26 (Th)
Generative learning algorithm, multivariate Gaussian, GDA, naive Bayes
Week 5


10/1 (Tu)
Example (naive Bayes, multivariate Gaussian), k-means clustering
10/3 (Th)
Gaussian mixture, EM
Week 6


10/8 (Tu)
general EM - convergence proof intuition
10/10 (Th)
midterm
Week 7


10/12-15 Fall Break
10/17 (Th)
More EM
Week 8

10/22 (Tu)
Principal Components Analysis, eigenface
10/24 (Th)
Independent Components Analysis
Week 9

10/29 (Tu)
bias/variance tradeoff, decision tree, entropy, random forest
10/31 (Th)
AdaBoost
Week 10


11/5 (Tu)

11/7 (Th)

Week 11


11/12 (Tu)
deep learning -- forward propagation
11/14 (Th)
deep learning -- back propagation
Week 12


11/19 (Tu)
deep learning  dl1.pdf
11/21 (Th)
deep learning dl2.pdf
Week 13

11/26 (Tu)
deep learning dl3.pdf
11/28 (Th)
No class: 11/27-12/1 Thanksgiving Break
Week 14


12/3 (Tu)
adversarial example, generative adversarial networks
12/5 (Th)

Week 15

12/6 class ends


Exam:
11:00TR — 12/10 Tue. 2:00PM-5:00PM