Computing Science 466/551:  Introduction to Machine Learning
          Sept-Dec 2009  	MEC 3-01  -- T Th 12:30-1:50pm
	  http://www.cs.ualberta.ca/~greiner/C-466/

Intructor: R Greiner  [x2-5461, greiner@cs.ualberta.ca, Ath 359]
	   Barnabas Poczos [x8-1435, poczos@cs.ualberta.ca, Ath 405]
      TAs: Shahab Jabbari Arfaee    Gabor		    Gabor Balazs
	{jabbaria  | gbalazs}@cs.ualberta.ca
                                                       
TEXTBOOKS:
  Required: Hastie/Tibshirani/Friedman: The Elements of Statistical Learning:
 	      Data Mining, Inference, and Prediction (2nd ed); Springer, 2009. 
  Rec'd:  Pattern Recognition and Machine Learning, C.M. Bishop, Springer, 2006.
	  Alpaydin: Introduction to Machine Learning; MIT Press, 2004.
	  Duda/Hart/Stork: Pattern Classification; Wiley, 2001.  
	  Mitchell: Machine Learning; McGraw Hill, 1997.
   + various URLs, handouts, ...
 ??      Reinforcement Learning, Sutton&Barto, online.

PURPOSE:
  This course provides a (near)graduate-level introduction to the field of
  Machine Learning, with an emphasis on the design on agents that can learn
  about their environment, to help them improve their performance on a
  range of tasks.  We will cover both 
   (1) PRACTICAL ASPECTS, including algorithms for learning linear
     classifiers, decision trees, SVMs, neural networks and belief networks;
     and general models such as reinforcement learning; and  
   (2) THEORETICAL CONCEPTS, including relevant ideas from statistics,
    inductive bias, Bayesian learning and the PAC learning framework. 
The assignments will include both hands-on experiments with various learning
algorithms (linear units, neural networks, decision trees, reinforcement
learnings) -- many in C/C++, JAVA or Matlab -- as well as theoretical analysis.

Tentative OUTLINE:
   Linear Classifiers/Regressors
   Artificial Neural Networks 
   Decision Trees 
   Support Vector Machine; Kernel Methods
   Evaluating Hypotheses (cross-validation)
   Bias/Variance, Regularization, Model selection, Feature selection...   
   Computational Learning Theory 
   Bayesian Decision Theory
   Ensemble Methods (Boosting, ...)
   Unsupervised learning
   Learning Graphical Models: Belief Nets, Markov Random Fields, ...
   Reinforcement Learning 

  +  other topics, possibly including
      Nearest neighbor; Learning Sets of Rules; ...
  + applications,  possibly including
      Adaptive Software, Computational Biology, ... 

EVALUATION:
    Assignments (tentatively: 4 -- both dry-lab + coding)    (70% total)
    Project (30%)    including presentations,
		      + meetings + write-up

Late Policy:  Assignments are due at the beginning of class. 
You will be "forgiven" a total of 4 late days for the assignments;
at most 2 for any single assignment.

OFFICE HOURS:
   Immediately after class (2:00-2:30pm, T/Th)   or  by arrangement.