CSC7333 Spring 2013 Class website - Machine
Learning
Class time: Tuesday and Thursday from 12:00pm
to 1:20pm
Classroom: Room 1116 Patrick Taylor Hall
Instructor: Dr. Jianhua Chen
Contact
info: E-mail: jianhua@csc.lsu.edu
Tel:
225-578-4340
Office:
3122-C Patrick Taylor Hall
Office Hours:
T, TH 2:00pm to 4:00pm
Other times:
By appointment
Grader: Mr. Forrest Osterman
Office
hrs: Monday 3pm to 5pm
Office:
168 D Coates Hall
Phone:
225-304-0669
Email:
forrest280z@gmail.com
More info.
about the class: class
announcement guidelines to
group projects
Required Text Book:
Machine Learning by Tom Mitchell,
Publisher: McGraw-Hill
web site of Tom Mitchell
Recommended Text Book:
The Elements of Statistical Learning by
T. Hastie, R. Tibshirani, J. Friedman
Quiz dates: Thursday Feb. 28, 2013 and Thursday April 11, 2013
Homework Assignments - Due by 5pm of
the due date
Homework1
Homework2
Homework3
Homework4
Attention for students who submit
homework electronically:
Please e-mail your homework to BOTH the instructor and the TA
and then submit a hard-copy within 24 hours of the due date/time
Late
homework submission policy:
Homework submitted after the due date will be accepted
within 3 days past the due date,
with a late penalty of 10 points per day late. No late
submission is accepted after that.
Reminder
link to DT and
feature learning paper
link to
Valiant's paper on learnability
link to the
random key (GA) paper
link to a tutorial
paper on NN some interesting applications
of NN
Other links
link
to the website of the book "Introduction to data mining" by Tan,
Steinbach and Kumar
lecture notes: What is ML
more on
introduction to ML
link to
a paper by Dr. Chen and colleagues on UUV control by NN and GA
link to a
paper on applying GA for playing checkers
link to a paper on
using GA for QAP
link to
a website containing intersting applications of GA
the iterated
prisoners dilemma game the
checkers game
the traveling sales man problem
the vehicle routing
problem the
quadratic assignment problem
link
to
Sutton and Barto's book on reinforcement learning
TD-gammon
link to ICML 2012 papers
link to ICML
2011 proceedings
link to ICML 2010
proceedings
link to
ICML 09 proceedings
link to ICML 08 proceedings papers
link
to ICML 07 proceedings
link
to
ICML06 proceedings
link
to
ICML05 proceedings
link
to
ICML04 proceedings
link
to
ICML03 proceedings
Group
Presentations:
Group1 Members
Topic: Predicting Income from Census
Data using Multiple Classifiers Slides
Group2
Members
Topic: Hand-written
Digits Recognition using Back-propagation Neural Networks
Group3
Members
Topic: Predicting
Alcoholism from EEG Data: A Comparative Study with Multiple
Classifiers and Support Feature Machines
(SFM) Slides
Group4
Members
Topic: Learning to play the game of Super Mario
Slides
Group5 Members
Topic: Design of a Click-Tracking Network for basic search
engine Slides
Group6 Members
Topic:
Hand-written Character Recognition using Parallel Computing,
Neural Networks and Genetic Algorithms
Group7
Members
Topic: Vehicle Detection using Bayesian Networks
Group 8 Members
Topic: Used
Cars classification and Classifiers Evaluation
Class Final Exam (open
book) Date/Time:
Monday May 6, 2013, from 3:00pm to
5:00pm