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