229352 | Statistical Learning for Data Science 2
Semester 2/2025 [Syllabus]
Time: Tue @ SCB4405 & Fr @ SCB4202 9.00am-10.30am
Instructor
-
Donlapark Ponnoprat
Office: STB304
Grading
- Lab 30%
- Midterm 35%. TBA
- Final 35%. THU MAR 26, 2026 15:30 - 18:30
Additional readings
There is no textbook requirement for this course. Here are some suggestions for additional readings- Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow by Aurélien Geron
- Dive into Deep Learning by Aston Zhang, Zack C. Lipton, Mu Li and Alex J. Smola
- Zero to Mastery Learn PyTorch for Deep Learning by Daniel Bourke
- UvA Deep Learning Tutorials by Phillip Lippe
| No. | Topic | Slides | Lab Notebook (Colab) |
|---|---|---|---|
| 1 | Introduction | CDF Estimation | |
| 2 | Data Preprocessing | Data Preprocessing | |
| 3 | k-nearest neighbors | 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 | Pytorch basics | |
| 9 | Neural Networks | Neural networks in PyTorch | |
| 10 | Convolutional neural networks | Image classification | |
| 11 | Transformers | LLM Fine-Tuning | |
| 12 | GANs and Diffusion Models | Diffusion models |