Intro to Machine Learning
ISTA 421 / INFO 521
Introduction to Machine Learning
[Fall 2017]


Schedule

Assignment Submission Instructions

# Day Topic/Slides Reading
1 M
8/21
Introduction FCML Ch1, ISLR Ch1
2 W
8/23
The Linear Model and Least Mean Squares
3 M
8/28
Higher Dimensions
4 W
8/30
Geometry of LLMS, Nonlinear response
M
9/4
Labor Day
5 W
9/6
Cross Validation, model selection, regularization ISLR Ch 5
6 M
9/11
Probability and Expectation FCML Ch 2, ISLR Ch 2
7 W
9/13
More Probability
8 M
9/18
Linear Gaussian Model, Maximum Likelihood
9 W
9/20
Properties of Linear Gaussian Model I
10 M
9/25
Properties of Linear Gaussian Model II
11 W
9/27
Introduction to Bayesian Modeling FCML Ch 3
12 M
10/2
Priors and Marginal Likelihood
13 W
10/4
Bayesian Linear Gaussian Model
14 M
10/9
Marginal Likelihood Model Selection
15 W
10/11
Midterm Exam
16 M
10/16
Logistic Regression FCML Ch 4, ISLR Ch 4
17 W
10/18
Estimation I - Gradient Methods
18 M
10/23
Estimation II - Laplace Approximation
19 W
10/25
Estimation III - Sampling, Metropolis-Hastings
20 M
10/30
Classification - Bayesian Classifier FCML Ch 5
21 W
11/1
Classification - Nearest Neighbors, Classifier Evaluation
22 M
11/6
Classification - SVMs I - Maximum Margin
23 W
11/8
Classification - SVMs II - Kernels
24 M
11/13
Neural Networks I - Perceptron and Backpropagation TBA
25 W
11/15
Neural Networks II - Autoencoders
26 M
11/20
Neural Networks III - Deep Learning
27 W
11/22
Gaussian Processes FCML Ch 8
28 M
11/27
Topic Modeling FCML Ch 9
29 W
11/29
Clustering - Kmeans and Mixture Models FCML Ch 6
30 M
12/4
Clustering - Gaussian Mixture Model and EM
31 W
12/6
Principle Components Analysis FCML Ch 7
M
12/11
Final Assignment Due