Ricardo Vilalta (rvilalta@uh.edu)
DH 2103
Mondays and Wednesdays 4:30 - 5:15 PM
5:15 PM - 6:30 PM DCH 1046
(713) 743-3614
| Zhenwei Dai |
|---|
| Office: DH 1036 |
| Office Hours: Weekdays 8:00 - 9:00 PM |
| Email: daizwhao@gmail.com |
Machine Learning is the study of how to build computer systems that learn from experience. It is a subfield of Artificial Intelligence and intersects with statistics, cognitive science, information theory, and probability theory, among others. The course will explain how to build systems that learn and adapt using real-world applications from industry and science (e.g., learning to classify astronomical objects, to predict medical diagnoses, to play chess, etc.).
The course will concentrate on the “statistical” aspects of machine learning. The course will be self-contained (i.e., I will not assume previous knowledge on topics such as optimization or information theory); a review session will precede those chapters in need of background knowledge. The main topics include regression, linear discriminants, Bayesian learning, neural networks, decision trees, support vector machines, ensemble learning, unsupervised learning, reinforcement learning, graphical networks, etc.
For more information visit the course on CANVAS.
| Graded Work | Weight |
|---|---|
Midterm Exams Homework Final Project |
50% 25% 25% |
| Dates to Remember | Event |
|---|---|
| January 13 | 1st week of class - check material on CANVAS |
| March 4 | 1st Midterm Exam |
| April 22 | 2nd Midterm Exam |
| May 4 | Final Project Due |
Note: There is no final exam in this course.
| Dates | Topic |
|---|---|
| January 13, 15 | Introduction to Machine Learning Special Topics Review, Overview Supervised Learning |
| January 22 | Concept Learning |
| January 27, 29 | Probabilistic Learning |
| February 3, 5 | Decision Trees I and II |
| February 10, 12 | Support Vector Machines, Neural Networks I |
| February 17, 19 | Neural Networks II |
| February 24, 26 | Deep Neural Networks, Ensemble Learning |
| March 4 | 1st Midterm Exam |
| March 9, 11 | Learning Theory I and II |
| March 16, 18 | Spring Break |
| March 23, 25 | Linear Regression, Gaussian Processes |
| March 30, April 1 | Evolutionary Search Stochastic Search |
| April 6, 8 | Reinforcement Learning |
| April 13, 15 | Unsupervised Learning |
| April 22 | 2nd Midterm Exam |
| May 4 | Final Project Due |
For more information visit the course on CANVAS.