Supplementary references are to the following textbook:
Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning (2nd edition)
It is available online and is referred to below as HTF.
| Jan 8 | Nearest neighbor classification [HTF 2.3, 7.10, 13.3]
Wed discussion: Python / NumPy tips |
| Jan 10 | A host of prediction problems [HTF 2.1, 2.2] |
| Jan 15 | Probability review I
Probability review_II |
| Jan 17 | The generative approach to classification [Handout (credit: Mark Gales, Cambdridge Univ.)]
Quiz 0 |
| Jan 22 | Linear algebra primer |
| Jan 24 | Multivariate generative modeling
HW0 due before midnight Jan 25 |
| Jan 29 | Multivariate generative modeling
Quiz 1 |
| Jan 31 | Linear regression [HTF 2.3.1, 3.2, 3.4] |
| Feb 5 | Linear regression [HTF 2.3.1, 3.2, 3.4] |
| Feb 7 | Logistic regression [HTF 4.4]
HW1 due before midnight Feb 7 |
| Feb 12 | Logistic regression [HTF 4.4]
Quiz 2 Wed discussion: PyTorch auto-differentiation |
| Feb 14 | Convexity |
| Feb 19 | Convexity
Discussion slides: Unconstrained optimization |
| Feb 21 | Linear classification [HTF 4.5, 12.1, 12.2]
Quiz 3 HW2 due before midnight on Feb 26 |
| Feb 26 | Linear classification [HTF 4.5, 12.1, 12.2] |
| Feb 28 | Multiclass linear prediction / Max-margin / Duality
Feedforward neural nets |
| Mar 5 | Feedforward neural nets
Informative projections [HTF 14.5] Quiz 4 |
| Mar 7 | Convolutional nets
HW 3 due Tuesday March 12 at midnight |
| Mar 12 | Dimensionality reduction / Informative projections
Quiz 5 |
| Mar 14 | Autoencoders and distributed representations / Classical methods: decision trees and kernels
HW 4 due Friday Mar 22 at midnight |