Machine Learning CSCI 5622

 

Fall 2004.

 

Location:          

Wednesdays 3:00pm-5:30pm ECCR 108

Instructor:

Professor Greg Grudic

Office:            

ECOT 525

Office Hours:

Tuesday and Wednesday 10:00 to 11:00

Phone:

303-492-4419

Email:

grudic@cs.colorado.edu

Course URL:

http://www.cs.colorado.edu/~grudic/teaching/CSCI5622_2004

 

How does an internet vender know which consumer group or individual to target for advertising? How do we find all pictures of Arnold Schwarzenegger in a large image database? Which gene is responsible for the cancer that runs in my family? Machine learning algorithms are at the heart of many computer applications designed to address these types of questions. Other important applications include Data Mining, Robotics, Profiling, User Interfaces, Document Characterization, Bioinformatics, and Linguistics.

 

Machine learning is the study of building systems that learn from experience. Machine learning algorithms are designed to address problem domains where good theoretical models don’t exist, but where empirical observations can be made.

 

This course covers four subfields of machine learning: supervised, semi-supervised, unsupervised and reinforcement learning. Emphasis is placed on a practical and theoretical understanding of the most widely used algorithms and their applications.

 

An important goal of this course to cover the basic material in machine learning to allow students to read and understand current research papers in machine learning.

 

Course Textbook: There is no single textbook that covers all the material in the course. However, a good reference text that touches on much of the material is:

 

The Elements of Statistical Learning, by Hastie, Tibshirani, Friedman

 

 

Grading:

 

Homework

45%

Midterm

20%

Project

25%

Class Participation

7%

Random Quizze

3%

 

Homework: These will consist of coding assignments. You must use MATLAB!!.  Unless you have a very good excuse, each day your assignment is late will take 1% off what the assignment is worth.

 

Midterm: It will consist of general questions on the machine learning algorithms covered to date. You will not be required to derive algorithms or prove theorems.

 

Project: Consists of building models from data I provide. Or a topic of your choice…

 

Class Participation: This consists of showing up for class and asking questions. Questions by email count as class participation!

 

Random Quizzes: There will be about 5 of these. Based on material covered in class. If you pay attention, these will be trivial…

 

 

Expected workload outside of class is 4 to 5  hours per week.

 

 

Disability Accommodations

If you qualify for accommodations because of a disability, please submit to me a letter from Disability Services in a timely manner so that your needs may be addressed. Disability Services determines accommodations based on documented disabilities. (303-492-8671, Willard 322, http://www.colorado.edu/disabilityservices).

 

Religious Accommodations

If you feel you can't be present at the final for religious reasons, please contact me as soon as possible to make arrangements.

 

Honor Code

The campus has adopted an Honor Code. It includes the following pledge which will be placed on all your exams and you will need to include on your assignments:

 

On my honor, as a University of Colorado at Boulder student, I have neither given nor received unauthorized assistance on this work.

 

Except when I specify otherwise, the assignments in this class will be done individually.  You may discuss the assignments with one another but the final product (program, paper, etc) must be yours alone.