This course provides an introduction to machine learning algorithms, which
learn from experience in a supervised, unsupervised, or reinforcement
learning paradigm. Specific algorithms include k-Nearest Neighbors,
Decision Trees, Support Vector Machines, Neural Networks, Bayesian Networks,
Clustering, Reinforcement learning, and Ensemble learning.
At the end of the course, students are able to:
- Apply several machine learning algorithms (supervised, unsupervised, and reinforcement learning) to data sets in standard format
- Select an appropriate algorithm to solve new problems
- Evaluate learning systems using cross-validation and statistical significance tests
- Use the Weka machine learning library (Java)
- Java programming experience (CS 203)
- Data structures and algorithms (CS 203, 312)
- Calculus and Propositional logic
- Recommended: Probability (Math 474) and Statistics (Math 274)
Time and place
Lectures: Saturdays, 9:00 a.m. - 1:10 p.m., ET-A210
Instructor: Kiri Wagstaff
Office hours: Saturdays, 1:15-2:45 p.m., ET-A220
Course website: http://www.wkiri.com/cs461/
- Midterm: February 2, 2008, during lecture
- There is no final exam (final project instead)
Your course grade will consist of:
- Homeworks (5): 50%
- Midterm: 25%
- Final project: 25%
Grading scale (scores are truncated, not rounded):
- A-: 90-93, A: 94-100
- B-: 80-82, B: 83-86, B+: 87-89
- C-: 70-72, C: 73-76, C+: 77-79
- D: 60-66, D+: 67-69
- No Credit: 0-59
- Homeworks are due on Thursdays at midnight. They may
be submitted one day late, with a 25% reduction in
score. No other late work will be accepted.
Cheating will not be tolerated. Simple guideline: all work you
turn in must be your own work, not anyone else's.
Cheating on any assignment or exam will be taken very seriously.
I encourage you to familiarize yourself with the Cal
State LA policies on Academic Honesty
, which includes examples
of what is considered cheating. In the context of this class, the
following examples also qualify:
Penalties for any cheating can include a grade of F for the course and
will be reported to the appropriate university authority.
- Using Google to find a solution to an assignment or
project. You are encouraged to use the web as a reference (e.g.,
"how does this Java method work?") but not as a solution-generator.
Take pride in your own work, and show me what you yourself can do.
- Collaborating on an assignment with classmates when this has not
been explicitly permitted in the assignment description. Use your
judgment. For solo assignments, questions of clarification are
certainly germane. "How did you get this to work?" and "Can I see
your code?" are off-limits.
Guidelines for Assignments
Each homework assignment indicates what should be
turned in, and in what format. In general, these guidelines apply:
- Programming assignments:
- You may use any development environment you like. However, submit
only your .java source files (not any project/IDE files). Your
code will be compiled and tested in a command-line environment,
so it is a good idea to check that this works prior to submission.
To receive full credit, your code must compile.
- Each Java source code file must contain a header with
your name, the class name and number, the quarter, the
name of the assignment, and the assignment due date.
- You will be graded on coding style. Use modularity, break long
lines, and comment your code to guide the reader in understanding
how it works. You may wish to browse
Programming Style Guidelines. Include Javadoc-style
comments in the header of each file and to describe each
guidelines for Javadoc comments).
- Written assignments:
- Submit your answers to written questions as .txt or .pdf files.
Word (.doc, .rtf, etc.) files will not be accepted.
Expectations of Students
- In class:
- Arrive to class on time.
- Turn cell phones off during lecture and lab.
Refrain from sending or receiving text messages, and
please do not check email or browse the web during lecture.
- Consume food/drink outside of the classroom.
- Complete assigned reading before lecture.
- Participate in class discussions and activities.
- Submit work on time. Late work is accepted only for
homeworks, one day after the deadline, and will incur a 25%
reduction in score.
- Be present for the midterm exam and final project presentations.
Makeup exams are not offered except in extreme circumstances.
Contact me immediately if you have a conflict.
- Outside of class:
- Place "CS 461" in the subject line of course-related email.
- Check email on a daily basis. You are responsible for being
aware of course announcements.
- Mandatory meeting after midterm with instructor to