"You can't stop the signal, Mal. Everything goes somewhere, and I go everywhere."
 Mr. Universe, from the movie "Serenity"
Course Description
This course is part of the University of Idaho series on Artificial Intelligence and Machine Learning which comprises these three courses: Artificial Intelligence, Machine Learning, and Evolutionary Computation. The course is basically new. (However, if you took AI under me a year ago, you cannot take this course because that course was a prototype for this course. There is a lot of overlap.)
We will focus on the machine learning half of Artificial Intelligence. This includes algorithms to learn behavior, classify data, and predict behaviors and/or data given past data. This is an implementation course. We will implement some of the important algorithms of machine learning and apply them to small problems (usually under 10K samples of data). This is a course about the algorithms and not about using premade tools to do machine learning and data mining. We will look at topics such as decision trees, neural networks, deep learning, Markov brains, and reinforcement learning. There will be lots of programming in C/C++ and algorithm work. Proficiency in programming in C/C++ is required as well as a solid understanding of undergrad math including an understanding of basic probability and statistics, calculus, and linear algebra. Algorithm descriptions will be given in Python and demonstrations in Mathematica (purchase not required).
Time: 1:302:30 Final: 504 students are required to create a final project and report. No final. Location: JEB 026 

Text:
Textbook: Machine Learning: An Algorithmic Perspective by Stephen Marsland (Second Edition!) 
Estimated Syllabus
This syllabus is an estimate of what we might cover this semester. The class varies from semester to semester to reflect new and interesting topics.
Week  Monday 
Topics/Links  Assignments  Comments  

wk 1  Jan 9  Artificial Intelligence, Machine Learning, Data Analytics the big picture, Learning vs Prediction, The classes of problems  Read Chapter 1  NO CLASS MONDAY  
wk 2  Jan 16  Treating the input, measuring the quality of output, are we successful  Read chapter 2  NO CLASS MONDAY  
wk 3  Jan 23  Neural networks and backprop  Read chapter 4  
wk 4  Jan 30  Neural networks continued, NEAT and HyperNEAT, deep learning in NN  Assignment 1  
wk 5  Feb 6  Dimensionality reduction and PCA  Read chapter 6  
wk 6  Feb 13  PCA continued  Assignment 3  
wk 7  Feb 20  Reinforcement learning  
wk 8  Feb 27  Reinforcement learning continued  
wk 9  Mar 6  Decision Trees  Assignment 4  
xx  Mar 13  SPRING BREAK!  
wk 10  Mar 20  Decision Trees and Kmeans  
wk 11  Mar 27  Kmeans continued  Assignment 5  
wk 12  Apr 3  Bayesian Networks  
wk 13  Apr 10  Bayesian Networks continued  Assignment 6  
wk 14  Apr 17  Association Rules  
wk 15  Apr 24  Markov Brains, Cartesian Genetic Programming  
wk 16  May 1  Stochastic Learning with the EM algorithm  
wk 17  May 8  Finals Week  Final: None 
References and Resources
 The peceptron Mathematica page seen in class
 A tutorial on make for general information
 A basic UNIX tutorial
 A tutorial on bit operations in C/C++
 The website for the text book
 University of California Irvine Machine Learning Repository
 A listing of the Mathematica example
 A detailed commented version of the backprop algorithm.