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
Time: Monday & Wednesday, 2:55-4:10pm
Room: Big Red
Class number: 12778
Course Requirements and Grading
- Grade Breakdown: Your grade will be determined by the assignments (30%), one prelim (30%), and a final exam (30%). There will also be quizzes (5%) and a playbook (5%).
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 7, 11:59 PM. 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 26 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.
- Playbook: Over the course of the semester, students will learn many techniques and "tools of the trade" that will be useful to them when they come across similar problems in their professional career. This year, students will explicitly compile their own "Guide to Methods of Machine Learning and Data Mining" reference based on material in the course. The intended audience will be a coworker, or perhaps the students' future selves.
- Final: May 9-14. The final exam is take-home, open-internet, but must be done on your own with thorough citations of all references used. The exam will be distributed Friday May 9 and due Wednesday May 14 at 4:30pm.
Please post your questions to Piazza instead of emailing the instructor or TAs. If you do send us email, please begin the subject line with [CS5785].