CSCI-567 Syllabus (fall 2008)
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.
Knowledge of probability theory and statistics, calculus, data structures, search algorithms (gradient descent, depth-first search, greedy algorithms).
The main textbook is:
The following textbooks are recommended reading:
Class RequirementsThe class grade will be based on absolute scoring and is broken down into the following components:
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.
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).