229352 | Statistical Learning for Data Science 2

Semester 1/2025 [Syllabus]

Time: Tue @ SCB4405 & Fr @ STB205 9.00am-10.30am


Instructor


Grading


Additional readings

There is no textbook requirement for this course. Here are some suggestions for additional readings
No.TopicSlidesLab Notebook (Colab)
1 IntroductionpdfCDF Estimation
2 Data PreprocessingpdfData Preprocessing
3 k-nearest neighborspdfkNN and Grid Search
4 Naive BayespdfNaive Bayes with Grid and Random Search
5 Decision Trees and Random ForestspdfDecision Trees and RFs
6 Support vector machinespdfDecision boundary of SVM
7 AdaBoost and Gradient Boosting MachinespdfBoosted Trees
8 Clustering and Gaussian Mixture ModelspdfPytorch basics
9 Neural NetworkspdfNeural networks in PyTorch
10 Convolutional neural networkspdfImage classification
11 TransformerspdfGemma 3 Fine-Tuning
12 GANs and Diffusion ModelspdfDCGANs and Diffusion model