"You can't stop the signal, Mal. Everything goes somewhere, and I go everywhere."
 Mr. Universe, from the movie "Serenity"
"If you torture data sufficiently, it will confess to almost anything."
 Fred Menger
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 department also offers related courses: CS474/574 Deep Learning and CS489 Semantic Web.
Prereq: Solid C/C++ programming skills  no... really, solid understanding of basic linear algebra, calculus, probability/statistics, UNIX skills desirable, CS210 (because many algorithms are explained using multiple programming languages) or instructor permission.
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 1000 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 a must as well as a solid understanding of undergrad math including an understanding of basic probability and statistics, probability distributions, calculus, and linear algebra. Algorithm descriptions will be given in Python and demonstrations in Mathematica (purchase not required).
Also available at the University of Idaho is CS477 which is how to use machine learning tools and Python toolkits to solve machine learning problems. The Math department also offers a course in the mathematics behind machine learning.
Hopefully this will be fun and entertaining.
Time: MWF 9:3010:30 Pacific Time Final: None Location: EP 204 Masks are required in class, vaccines highly recommended 

Textbook:
Machine Learning: An Algorithmic Perspective by Stephen Marsland (Second Edition!) 

Optional Ref:
The Quick Python Book 3nd Ed. by Naomi Ceder Really great and easy to read book. Fine index. 
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 10  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 17  Examining and cleaning the input, missing data, scaling, curse of dimensionality  Read chapter 2  NO CLASS MONDAY 
wk 3  Jan 24  The Matrix Library, Simple perceptron, single layer neural networks, matrix version of single layer algorithm, sigmoid functions, demonstration of algorithms running  Read chapter 3, Assignment 0  
wk 4  Jan 31  training, validation, testing, measures of quality, ROC space, recursive networks, evolutionary training of neural networks  Assignment 1  
wk 5  Feb 7  Multilayer NN and backprop, convergence behavior and demo. Time series and deep neural networks  Assignment 2, Read chapter 2.4  
wk 6  Feb 14  Dimensionality reduction and PCAs. Handout on the PCA algorithm that might be useful.  Read chapter 6.2 on  
wk 7  Feb 21  PCA and Linear Discriminant Analysis (LDA)  Read chapter 6, Assignment 3  NO CLASS MONDAY 
wk 8  Feb 28  Dimensionality reduction and PCAs. Handout on the PCA algorithm that might be useful.  Read chapter 6.1, chapter 7  
wk 9  Mar 7  Nearest Neighbor Methods and KDTrees  Read chapter 7, Assignment 4  
wk 10  Mar 14  SPRING BREAK!  NO CLASS THIS WEEK  
wk 11  Mar 21  KMeans,EM  Read Chapter 7.2 Assignment 5  
wk 12  Mar 28  EM, Decision Trees  Read Chapters 14.1 and 12 Assignment 6  
wk 13  Apr 4  Entropy/Information, Decision Trees, Random Forests  Read chapter 13, Assignment 7  
wk 13  Apr 11  Ensemble Learning  
wk 14  Apr 18  Reinforcement Learning, QLearning, Sarsa, eligibility traces  
wk 15  Apr 25  eligibility traces, Qlearning in games  NO CLASS THURSDAY  
wk 16  May 2  Market Basket Analysis, Stochastic Learning with the EM algorithm or Text analysis  
wk 17  May 9  Finals Week  Final: None 
References and Resources
 mat.tar or mat.zip is a C++ matrix object with strong debugging features. Uses included random number generators. WARNING: the tar/zip contains a file named makefile and two example cpp code files. Do not overwrite your own makefile by exploding this archive over your makefile.
 How to debug seg faults using gdb
 A tutorial on make for general information
 Curse of Dimensionality notes
 Perceptron Notes
 Some notes on the formulas for basic feedforward neural networks.
 List of Python Tutorials
 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.
 Handout on the PCA algorithm that might be useful.
Some Videos
Services
 The class has its own machine with more uptodate compilers than maybe in other classes! The machine is: cs475.cs.uidaho.edu. Give it a try and report any problems to cshelp@uidaho.edu if there is a problem. The machine is only visible on campus. If you are outside the university firewall you will need to VPN onto campus. To be allowed to VPN, you need to sign up with IT by contacting helpdesk@uidaho.edu to have your student account added to the VPN group.
 CSAC is the Computer Science Assistance Center and is staffed with people to help with things like simple programming questions or problems with logging onto a CS machine. They are now located in JEB 211.
 Homeworks for CS475 are submitted for testing using this secure submission page. The last submission before the due date will be graded. This is the only place to submit homeworks.
 How Testing Works