| Mon 26 Jan |
1 |
Introduction, Kaggle |
| Wed 28 Jan |
2 |
Decision boundaries, sklearn pipelines,
kNN classifier |
| Mon 2 Feb |
3 |
Validation, also quick intro to
Pandas. |
| Wed 4 Feb |
4 |
Subsampling, Decision Trees |
| Mon 9 Feb |
5 |
Cross-validation, Bootstrap, Boosting,
Bagging, Random Forests |
| Wed 11 Feb |
6 |
Grid search, multi-class classifiers |
| Mon 16 Feb |
|
No class |
| Wed 18 Feb |
7 |
Competition-specific analysis, PCA |
| Mon 23 Feb |
8 |
Stacking, Kernels, Support Vector
Machines |
| Wed 25 Feb |
9 |
New competition, Intro to regression,
parallelize, GLM and transformed targets |
| Mon 2 Mar |
10 |
Regularization, Feature Selection,
Polynomial Regression |
| Wed 4 Mar |
11 |
Cross-validation for time-series |
| Mon 9 Mar |
12 |
Inner and Outer Database Joins |
| Wed 11 Mar |
13 |
Diagnostic plots with Yellowbrick |
| Mon 16 Mar |
14 |
Neural Networks |
| Wed 18 Mar |
15 |
Backpropagation, Convolutional Neural
Networks |
| Mon 23 Mar |
16 |
Neural Networks with TensorFlow, Keras and
TPU |
| Wed 25 Mar |
17 |
Midterm |
| Mon 30 Mar |
18 |
Dealing with overfitting |
| Wed 1 Apr |
|
No class |
| Mon 6 Apr |
|
No class |
| Wed 8 Apr |
|
No class |
| Mon 13 Apr |
19 |
Optimizers |
| Wed 15 Apr |
20 |
Transfer Learning |
| Mon 20 Apr |
21 |
Ethics |
| Wed 22 Apr |
22 |
word2vec and autoencoders |
| Mon 27 Apr |
23 |
Recurrent neural networks |
| Wed 29 Apr |
24 |
Bayesian Statistics |
| Mon 4 May |
25 |
Bayesian Classifiers |
| Wed 6 May |
26 |
Reinforcement Learning |
| Mon 11 May |
27 |
Computational Creativity, Deep Dream,
Style transfer, GAN |
| Wed 13 May |
28 |
Review |