### Current Teaching - Fall 2017

#### Introduction to Data Science

### Previous teaching

#### EPI 9650B/EPI 4715B: Design and Analysis of Clinical Trials

#### CS 4437B/CS 9637B: Introduction to Data Science

#### CS 886: Advanced Topics in Artificial Intelligence:

Applied Machine Learning - Fall 2014

#### CS 135: Designing Functional Programs - Fall 2014

**All** CS135 info is located on the course webpage.

#### CS 136: Elementary Algorithm Design and Data Abstraction - Fall 2013

**All** CS136 info is located on the course webpage.

#### CS 886: Advanced Topics in Artificial Intelligence:

Applied Machine Learning

Here are the slides from my CS 886 course advertisement.

#### CS 136: Elementary Algorithm Design and Data Abstraction - Fall 2012

**All** CS136 info is located on the course webpage.

#### CS 886: Advanced Topics in Artificial Intelligence:

Applied Machine Learning - Fall 2012

#### CS 886: Advanced Topics in Artificial Intelligence:

Applied Machine Learning - Winter 2012

#### CS 136: Elementary Algorithm Design and Data Abstraction - Fall 2011

#### U of A CS 466 - Introduction to Machine Learning - Winter 2008

Guest lecture: Introduction to Gaussian Processes.#### Introduction to Machine Learning - Spring 2007

Reykjavík University

In the age of Google and Netflix, enabling computers to learn from data has become a prerequisite for solving many interesting problems. This course will cover the foundations of machine learning along with basic algorithms used in supervised learning, unsupervised learning, and reinforcement learning. Topics covered will include decision trees, support vector machines, and k-nearest-neighbours for classification, as well as k-means clustering for unsupervised learning. Reinforcement learning topics will include offline and online algorithms for agents learning to behave optimally in uncertain environments. Students will have the opportunity to gain hands-on experience with techniques in each of these areas.

#### Topics and Lecture Notes

These are drawn from three main sources: Andrew Moore's excellent series of tutorials in machine learning, Russ Greiner's teaching materials, and Rich Sutton's slides on reinforcement learning.

- Introduction
- Alpaydin Chapter 1

- Decision Trees
- Alpaydin Chapter 9.1-9.3
- AIxploratorium

- PAC
Learning
- Alpaydin Chapter 2.3

- VC
Dimension
- Alpaydin Chapter 2.2
- Wikipedia entry on VC Dimension

- Overfitting and Cross
Validation
- Alpaydin Chapter 14.1 - 14.2

- Probabilities, Bayes Classifiers - Part 1
- Probabilities,
Bayes Classifiers - Part 2
- Alpaydin Appendix A.1, A.2.1 - A.2.4
- Wikipedia entry on Naive Bayes
- Alpaydin Chapter 3.7 - but this is more complicated than we need. If you are interested in general Bayes nets, look here.

- Support Vector Machines - Part 1
- Support Vector
Machines - Part 2
- Alpaydin Chapter 10.9

- Reinforcement
Learning - Part 1
- Alpaydin Chapter 16
- Sutton and Barto Chapters 1, 2, 3

- Reinforcement
Learning - Part 2: TD
- Alpaydin Chapter 16.5
- Sutton and Barto Chapter 6

- Exam Review

#### Assignments

Assignments are due at the
beginning of class on the date specified.

Late
assignments will not be accepted.

- Assignment 1 - Due on April 30th
- Assignment 2 - Due on May 7th
- Assignment 3 - Due
on May 14th
*at noon via e-mail*- RUGridWorldDemo Reinforcement Learning Envrionment
- Assignment 3 Part 2 sample solution - PDF
- Assignment 3 Part 2 sample solution - Excel

Required text: | Supplemental text: |
---|---|

Introduction
to Machine Learning Ethem Alpaydin The MIT Press ISBN-10: 0-262-01211-1 ISBN-13: 978-0-262-01211-9 | Reinforcement
Learning: An Introduction Richard S. Sutton and Andrew G. Barto The MIT Press ISBN-10: 0-262-19398-1 ISBN-13: 978-0-262-19398-6 FULL TEXT AVAILABLE ONLINE |