Syllabus
Textbooks
We will assign readings from the following texts.
- Deep Learning by Goodfellow et al.
- Machine Learning Probabilistic Perspective by Kevin Murphy
Other Supplemental Texts
- Machine Learning, by Tom Mitchell. Great introduction to machine learning
Web Resources
- Theoretical Computer Science Cheat Sheet Definitions Series has some useful formulae for this class, but also for others, including algorithms.
Schedule
| Week | Start Date | Topic to be covered | Project Milestones | Homework |
|---|---|---|---|---|
| 1 | 5/14/2020 | Introduction, data, real world issues, linear algebra, and statistics | ||
| 2 | 5/21/2020 | Numerical methods, optimization, calculus | HW 1 [Release] | |
| 3 | 5/28/2020 | Supervised Machine learning | ||
| 4 | 6/4/2020 | Feedforward neural networks, Keras, Tensorflow | Project 1 due | HW 1 due |
| 5 | 6/11/2020 | CNNs | HW 2 [Release] | |
| 6 | 6/18/2020 | Regularization over neural networks and other practical topics | ||
| 7 | 6/25/2020 | Optimization | HW 2 due | |
| 8 | 7/2/2020 | Sequence learning: Markov models | ||
| 9 | 7/9/2020 | HMMs, LSTMs | Project 2 due | |
| 10 | 7/16/2020 | Dimensionality reduction | HW 3 [Release] | |
| 11 | 7/23/2020 | Unsupervised Learning | ||
| 12 | 7/30/2020 | Reinforcement learning | HW 3 due | |
| 13 | 8/5/2020 | Wrap up | Final Exam [Aug 9] |
Cyril Weerasooriya
PhD Student
My research interests include distributed robotics, mobile computing and programmable matter.