CSCI 567
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CSCI-567 Syllabus (fall 2008)


General Information

Location:GFS 118
Times:T-Th, 5:00pm-6:20pm
Instructor:Sofus A. Macskassy.
Office:SAL 216
Office Hours:By appointment.
Send me an email and I will be in the office before class.
Phone:310-414-9849 x247.
Email:macskass@usc.edu
Teaching assistant: Cheol Han
Office Hours: M 2-3, W 11-12
E-mail: cheolhan at usc dot edu
Class web page:
http://www-scf.usc.edu/~csci567
This is where class notes, announcements and homeworks will be posted.

Goals

When you have completed this course, you should be able to apply machine learning algorithms to solve machine learning algorithms for both iid and sequential data problems of moderate complexity. You should also be able to read current research papers in machine learning and understand the issues raised by current research in supervised learning.

 

Prerequisites

Knowledge of probability theory and statistics, calculus, data structures, search algorithms (gradient descent, depth-first search, greedy algorithms).

Some AI background is recommended, but not required.

Reference Materials

The main textbook is:

  • Ethem Alpaydin, "Introduction to Machine Learning", MIT Press, 2004.
    Errata: http://www.cmpe.boun.edu.tr/~ethem/i2ml/

    The following textbooks are recommended reading:

    Lecture notes and other relevant materials will be made available on this web page and/or distributed in class as needed.

    Class Requirements

    The class grade will be based on absolute scoring and is broken down into the following components:

    • Weekly quizzes - 10%
    • Homework - 5 assignments - 20%
    • A midterm in-class examination - 20%
    • A final exam (not cumulative) - 20%
    • A final project - 30%

    The assignments will require solving problems related to the course content, as well as experimentation using Weka.

    The final project will involve choosing a topic, either from a list of suggestions that will be provided or based on your own interests. You will be required to read papers related to the topic, implement or find available implementations of the algorithms discussed, and perform experimental work comparing these algorithms. At the end of the semester, you will write a final report and you will prepare a 10-20 minute class presentation of the topic you investigated. The presentations will be scheduled outside of our lecture time, during December.

    Assignments

    Written Homework and Programs are due at the beginning of class.

    Each student is responsible for his/her own work. The standard departmental rules for academic dishonesty apply to all assignments in this course. Collaboration on homeworks and programs should be limited only to answering questions that can be asked and answered without using any written medium (e.g., no pencils, instant messages, or email).