Discussion sections are a chance to go over the material in more depth, ask questions, and work on problems together. They will be led by TAs for the course. Section participation (starting week 2) is worth 3% of your final grade.
We realize that there will be times where you may not be able to attend section. To account for this, we will drop section attendance for one week, meaning you can miss one section and not have it impact your final grade. Additionally, you may attempt the problems on your own and send your work to your section TA to earn full credit.
Steps to follow:
| Date | Content | Resources |
|---|---|---|
| 4/2 | Python, Probability Fundamentals, Linear Algebra | section slides, section worksheet, worksheet solutions, python intro (source), python intro (html), intro to python slide deck |
| 4/9 | Data Normalization and Linear Algebra | section slides, section worksheet, worksheet solutions |
| 4/16 | Vector Calculus, Bias and Estimation | |
| 4/23 | Train-Test Splitting, Generalized Least Squares Regression, MAP as Regularization | |
| 4/30 | Midterm Prep (+ Cross Validation, Convexity) | |
| 5/7 | Intro to PyTorch, SGD | |
| 5/14 | Kernels, PyTorch | |
| 5/21 | Neural Networks | |
| 5/28 | PCA, SVD, Convolutional Neural Networks (including HW4 background) | |
| 6/4 | Final Review, Next Steps |