Intro to Machine Learning
ISTA 421 / INFO 521
Introduction to Machine Learning
[Fall 2018]


Schedule

Assignment Submission Instructions

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