Intro to Machine Learning
ISTA 421 / INFO 521
Introduction to Machine Learning
[Fall 2019]


Schedule

Assignment Submission Instructions

# Day Topic/Slides Reading
1 M
8/26
Introduction FCML Ch1, ISLR Ch1
2 W
8/28
The Linear Model and Least Mean Squares
M
9/2
Labor Day
3 W
9/4
Higher Dimensions
4 M
9/9
Geometry of LLMS, Nonlinear response
5 W
9/11
Cross Validation, model selection, regularization ISLR Ch 5
6 M
9/16
Probability and Expectation FCML Ch 2, ISLR Ch 2
7 W
9/18
More Probability
8 M
9/23
Linear Gaussian Model
9 W
9/25
Maximum Likelihood
10 M
9/30
Properties of Linear Gaussian Model I
11 W
10/2
Properties of Linear Gaussian Model II
12 M
10/7
Introduction to Bayesian Modeling FCML Ch 3
13 W
10/9
Priors and Marginal Likelihood
14 M
10/14
Bayesian Linear Gaussian Model
15 W
10/16
Marginal Likelihood Model Selection
16 M
10/21
Review
17 W
10/23
Midterm Exam
18 M
10/28
Logistic Regression FCML Ch 4, ISLR Ch 4
19 W
10/30
Estimation I - Gradient Methods
20 M
11/4
Estimation II - Laplace Approximation
21 W
11/6
Estimation III - Sampling, Metropolis-Hastings
22 M
11/11
Veterans Day
23 W
11/13
Classification - Bayesian Classifier FCML Ch 5
24 M
11/18
Classification - Nearest Neighbors, Classifier Evaluation
25 W
11/20
Classification - SVMs I - Maximum Margin
26 M
11/25
Classification - SVMs II - Kernels
27 W
11/27
Neural Networks I - Perceptron and Backpropagation TBA
28 M
12/2
Neural Networks II - Autoencoders
29 W
12/4
Clustering - Kmeans and Mixture Models FCML Ch 6
30 M
12/9
Clustering - Gaussian Mixture Model and EM
31 W
12/11
Principle Components Analysis FCML Ch 7
M
12/13
Final Assignment Due (no in-class exam)