• This page is currently under construction.
• The university learning platform LEA is still the official source of information.
• Testing with classroom.github.com is being done to see if it helps manage the projects for this course.

####Topics (taken from 2015 summer semester slide titles):

1. Introduction to Learning and Adaptivity
2. Concept Learning
3. Decision Tree Learning
4. Biological Neural Networks
5. Artificial Neural Networks
6. Self-Organizing Maps
7. Reinforcement Learning
8. Genetic Algorithms

http://www.cmpe.boun.edu.tr/~ethem/i2ml2e

1. Introduction
2. Supervised Learning
3. Bayesian Decision Theory
4. Parameteric Methods
5. Multivariate Methods
6. Dimensionality Reduction
7. Clustering
8. Nonparametric Methods
9. Decision Trees
10. Linear Discrimination
11. Multilayer Perceptrons
12. Local Models
13. Kernel Machines
14. Bayesian Estimation
15. Hidden Markov Models
16. Graphical Models
17. Combining Multiple Learners
18. Reinforcement Learning
19. Design and Analysis of Machine Learning Experiments

http://alex.smola.org/drafts/thebook.pdf svn://smola@repos.stat.purdue.edu/thebook/trunk/Book/thebook.tex

Everyone hates getting a bunch of newsletter mail, but I hightly recommend signing up to some of the nvidia newsletters. http://www.nvidia.com/object/newsletter.html, even if you don't have an Nvidia GPU they're often sharing very interesting articles and projects for Machine Learning, and Robotics.

• http://www.nvidia.com/object/machine-learning.html
• https://developer.nvidia.com/deep-learning
• https://developer.nvidia.com/deep-learning-software

If you have an Nvidia GPU and are using 14.04, check out their DIGITS project.

• https://developer.nvidia.com/digits
• https://github.com/NVIDIA/DIGITS