文档介绍：General Information
Course Id: COSC6342 Machine Learning
Time: Tuesdays and Thursdays 2:30 PM – 4:00 PM
Professor: Ricardo Vilalta (******@)
Office: PGH 573
Telephone: (713) 743-3614
Office Hours: Tuesdays, Thursdays 1:30 PM – 2:30 PM
Textbook
Textbook: “Introduction to Machine Learning”
by Ethem Aplaydin, 1st Edition. MIT Press, 2004
Additional Reading:
“Pattern Classification” by Duda, Hart, and Stork
2nd Edition, Wiley-Interscience, 2000.
“Computer Systems that Learn”
by Kulikowski and . Edition,
Morgan Kaufmann, 1991
“Machine Learning” by Tom Mitchell
1st edition, McGraw-Hill, 1997.
Grading
Midterm Exams 60%
Homework 20%
Project 20%
There is no final exam
NOTE: PLAGIARISM IS NOT TOLERATED.
Homework
Homework will include mainly exercises from the textbook
The project will be a report on some area in machine learning you
find most interesting.
You can either report on some novel experiments after applying an
algorithm on a database or attempt a theoretical analysis.
The report must include a short survey of related work with the
corresponding list of references.
Dates to Remember
October 5 1st Midterm Exam
November 30 2nd Midterm Exam
November 7,9 No class (attending workshop)
November 23 No class (Thanksgiving Holiday)
December 5 Submit Project Report
How to eed in Class
In case you miss a class, read the chapter corresponding to that class.
Consult the professor during his office hours if you have questions.
The exams will cover the material covered in class only, but it
is important to read the textbook thoroughly.
Assignments will prepare you well for the exam.
Exams should not be a problem if you have been following the classes
and reading the textbook.
Familiarize with the software; think what aspect of machine learning
you like the most soon.
What is Machine Learning?
Where does machine learning fit puter science?
What is machine learning?
Where can machine learning be applied?
Should I care ab