DR. MUGIZI ROBERT RWEBANGIRA -INSTRUCTOR

Charletta	Fola	Kevon	Michelle	Qi’Anne	Ronald	Tacuma

Syllabus

Grades

SPRING 2010 CLASS

Date	Day	Lec #	Topic	Materials
1/10	T	1	What is machine learning? Spam filtering as a typical application.	Andrew Ng Video, Machine Learning Wikipedia
1/13	H	2	Probability Review	hw1, hw1sol, Probability Slides, Probability Video,
1/18	T	3	Conditional Probability, Bayes’ Rule	Bayes’ Rule, Bayes’ Rule
1/20	H	4	Naïve Bayes	hw2, hw2sol, Wiki, A Plan for Spam, Bayesian Spam Filtering
1/25	T	5	Octave/Matlab tutorial	hw3
1/27	H		!!CLASS CANCELLED DUE TO SNOW!!
2/1	T	6	Octave File Input/Ouput
2/3	H		Test 1	test1, test1sol
2/8	T	7	Octave Review
2/10	H	8	Octave Functions
2/15	T	9	Octave
2/17	H	10	Octave	hw4, hw4sol
2/22	T	11	Octave
2/24	H		Test 2	test2, test2sol
3/1	T	12	Bayes Nets	wiki, tutorial
3/3	H	13	Bayes Nets	hw5, hw5sol
3/8	T	14	Bayes Nets
3/10	H		MIDTERM	midterm, midtermsol
3/15	T		SPRING BREAK
3/17	H		SPRING BREAK
3/22	T	15	Perceptron	hw6, hw6sol
3/24	H	16	Perceptron
3/29	T	17	Linear Regression	Regression video, Regression slides
3/31	H	18	Linear Regression	hw7, hw7sol
4/5	T	19	Linear Regression	hw8, hw8sol
4/7	H	20	Multivariable linear regression
4/12	T	21	Polynomial Regression	hw9, hw9sol
4/14	H		Test 3	test3, test3sol
4/19	T	22	Reinforcement Learning, Markov Systems, Markov Decision Processes(MDP)	wiki, Markov Decision Process slides
4/21	H	23	Markov Decision Processes, Review	hw10, hw10sol
4/26	T		FINAL	fin, finsol