This site is for the 2013 class. Did you mean to visit the Spring 2014 class?
Learn and apply key concepts of modeling, analysis and validation from Machine Learning, Data Mining and Signal Processing to analyze and extract meaning from data. Implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. Gain working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, association rule mining and dimensionality reduction.
CS 2800 or equivalent plus experience programming with Python or Matlab, or permission of the instructor.
We will also use of selected readings from several other sources.
Room & Time
Tuesday & Thursday 11:40am-12:55pm, Room: Big Red, Class Number: 16825.
Course Requirements and Grading
- Grade Breakdown: Your grade will be determined by the assignments (1/3), one prelim (1/3) and a final project (1/3).
Homework: There will be approximately four assignments. Each assignment will have a “target date” for completion but the actual due date for turning in all of the assignments is May 3. Thus you can work at your own pace, but it is a good idea to stick to the target dates and come by office hours periodically to check your progress.
Collaboration: You are encouraged (but not required) to work in groups of 2 on each assignment. Indicate the name of your collaborator at the top of each assignment and cite any references used (including articles, books, code, websites, and personal communications). You may submit just one writeup for the group. Remember not to plagiarize; all solutions must be written by members of the group.
Prelim: March 14 (Thursday) in class. The exam is closed book but you are allowed to bring one sheet of notes with writing on the front and back. You can use a calculator.