229352 | Statistical Learning for Data Science 2
Fall 2022
Time: Tu & Fr 9.00am-10.30am
Instructor
- Donlapark Ponnoprat
Office: STB304
- Dive into Deep Learning by Aston Zhang, Zack C. Lipton, Mu Li and Alex J. Smola
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
- Zero to Mastery Learn PyTorch for Deep Learning by Daniel Bourke
- UvA Deep Learning Tutorials by Phillip Lippe
- Open Machine Learning Course by Yury Kashnitsky et al.
Lecture | Topic | Annotated Slides | Labs |
---|---|---|---|
1 | Probability review | Lecture 1 | Lists, Tuples and Dictionaries |
2 | Information theory | Lecture 2 | Iterations and Error Handling |
3 | Data Preprocessing | Lecture 3 | Data Preprocessing |
4 | k-nearest neighbors | Lecture 4 | |
5 | Naive Bayes | Lecture 5 | |
6 | Decision Trees and Random Forests | Lecture 6 | Grid Search Cross-Validation |
7 | Support vector machines | Lecture 7 | |
8 | AdaBoost and Gradient Boosting Machines | Lecture 8 | SVMs and Boosted Trees |
9 | Clustering and Gaussian Mixture Models | Lecture 9 | HuggingFace Spaces |
10 | Neural Networks | Lecture 10 | Intro to PyTorch |
11 | Convolutional neural networks | Lecture 11 | CNNs |
12 | Recurrent neural networks | Lecture 12 | LSTMs |
13 | Transformers | Lecture 13 | Transformers |
14 | Generative Adversarial Networks and Diffusion Models | Lecture 14 | DCGANs |