229352 | Statistical Learning for Data Science 2
Fall 2024
Time: Tue @ SCB4405 & Fr @ STB205 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.
| No. | Topic | Slides | Lab Notebook (Colab) |
|---|---|---|---|
| 1 | Introduction | Lists, Tuples and Dictionaries | |
| 2 | k-nearest neighbors | Iterations and Error Handling | |
| 3 | Data Preprocessing | kNN and Grid Search | |
| 4 | Naive Bayes | Naive Bayes with Grid and Random Search | |
| 5 | Decision Trees and Random Forests | Decision Trees and RFs | |
| 6 | Support vector machines | Decision boundary of SVM | |
| 7 | AdaBoost and Gradient Boosting Machines | Boosted Trees | |
| 8 | Clustering and Gaussian Mixture Models | [Deploy through Gradio and HF] [App files] | |
| 9 | Neural Networks | NNs in PyTorch | |
| 10 | Convolutional neural networks | Image classification | |
| 11 | Recurrent neural networks | Lyrics generation | |
| 12 | Transformers | GPT from scratch | |
| 13 | Generative Adversarial Networks and Diffusion Models | DCGANs and Diffusion model |