Schedule

The schedule might change subject to class progress.

week	(Fri) 5:30pm - 6:20pm	(Tue) 4:30pm - 6:20pm
1	(Sep 2) introduction	(Sep 6) linear perceptron
2	(Sep 9) logistic regression	(Sep 13) multi-class, support vector machines
3	(Sep 16) holiday no class	(Sep 20) decision trees
4	(Sep 23) review: calculus, optimization	(Sep 27) KNN, Bayesian
5	(Sep 30) review: probability	(Oct 4) Data pre-processing
6	(Oct 7) Dimensionality reduction	(Oct 11) Dimensionality reduction
7	(Oct 14) review: common assignment mistakes and questions	(Oct 25) Model tuning and evaluation
8	(Oct 28) Info day, no class	(Nov 1) Ensemble learning
9	(Nov 4) Ensemble learning	(Nov 8) Regression analysis
10	(Nov 11) Regression analysis	(Nov 15) Clustering analysis
11	(Nov 18) Neural networks	(Nov 22) Neural networks
12	(Nov 25) Neural networks	(Nov 29) Case study

We refer to the following books along with additional resources.

• PML: Python Machine Learning, by Sebastian Raschka
  • Key ideas and code
• IML: Introduction to Machine Learning, by Ethem Alpaydin
  • More math and algorithm details

Table of Contents Introduction Linear perceptron Logistic regression Support vector machines Decision trees K-nearest neighbors Bayesian estimation Data pre-processing Dimensionality reduction Model tuning and evaluation Ensemble learning Regression analysis Clustering analysis Neural networks Case study: sentiment analysis Reinforcement learning

Introduction

Reading

• Slides
• PML Chapter 1
• IML Chapter 1

Math

• Slides

Coding

• Install anaconda, git
• Review/learn python, ipynb
• See README.md for quick instructions.

Assignment

• ex01 - due Sep 16 2016

Linear perceptron

Reading

• Slides
• PML Chapter 2
• IML Chapter 10.1-10.4, linear discrimination

Assignment

• ex02 - due Sep 28 2016

Logistic regression

Reading

• Slides
• PML Chapter 3

Support vector machines

Reading

• Slides
• PML Chapter 3
• IML Chapter 13-1 to 13.7
• The kernel trick

Decision trees

Reading

• Slides
• PML Chapter 3
• IML Chapter 9

K-nearest neighbors

Reading

• Slides
• PML Chapter 3

Bayesian estimation

Reading

• Slides
• IML Chapter 3, 4.1-4.4

Assignment

• ex03 - due Oct 19 2016
• only the first two assignments are under github for public domain reference; the rest would be available under Moodle

Data pre-processing

Reading

• Slides
• PML Chapter 4
• IML Chapter 6.2

Dimensionality reduction

Reading

• Slides
• PML Chapter 5
• IML Chapter 6.3, 6.6, 13.12

Assignment

• ex04 - due Nov 2 2016

Model tuning and evaluation

Reading

• Slides
• PML Chapter 6

Ensemble learning

Reading

• Slides
• PML Chapter 7
• IML Chapter 17.1-17.7

Assignment

• ex05 - due Nov 16 2016

Regression analysis

Reading

• Slides
• PML Chapter 10

Clustering analysis

Reading

• Slides
• PML Chapter 11
• IML Chapter 7.3, 7.7, 7.8

Assignment

• ex06 - due Dec 14 2016

Neural networks

Slides

• ch12
• ch13

Reading

• PML Chapter 12, 13
• IML Chapter 11
• Neural networks and deep learning

Case study: sentiment analysis

Reading

• Slides
• PML Chapter 8

Reinforcement learning

Reading

• IML Chapter 18