Machine Learning with Discriminative Methods

Alex Berg

Spring 2015

T/Th 12:30-1:45 in Sitterson 014

Syllabus

(h/t Maxim Raginksy for the web design and excellent notes.)


Thu. Jan. 8
[ Slides ]
Introduction
Thu. Jan. 15
[ Slides ]
Of Machine Learning and Loss
Tue. Jan. 20
[ Slides ]
PAC Learning and Tail Bounds Intro
Thu. Jan. 22
[ On Board ]
Go over learning and tail bounds.
Tue. Jan. 27
[ On Board ]
Empirical Risk Minimization
Thu. Jan. 29
[ Slides ]
Doit 1
Tue. Feb. 2
[ Slides ]
Linear Models 1
Thu. Feb. 4
[ Slides ]
Linear Models 2
Tue. Feb. 10
[On Board]
Reading review
Thu. Feb. 12
[On Board]
Feature selection review
Tue. Feb. 17
[in snow]
It's full of snow.
Thu. Feb. 19
[on board]
Perceptron and SVMs
Tue. Feb. 24
[on board]
SVM intro, projects
Thu. Feb. 26
[in snow]
It's full of snow.
Tue. Mar. 3
[slides]
Non-linear Classifiers, midterm announcement
Tue. Mar. 17
Applying machine learning, midterm handed out
Thu. Mar. 19
In class midterm
Tue. Mar. 24
[on board]
Optimization 1
Thu. Mar. 26
[on board]
Optimization 2
Tue. Mar. 31
[on board]
Optimization 3
Thu. Apr. 2
[slides]
Structured Prediction
Tue. Apr. 7
[slides]
Deep learning 1
Thu. Apr. 9
[on board]
Deep learning 2
Tue. Apr. 14
[slides]
Deep learning 3
Thu. Apr. 16
[slides]
Presentations 1
Thu. Apr. 21
[slides]
Presentations 2