229352 | Statistical Learning for Data Science 2

Fall 2022

Time: Tu & Fr 9.00am-10.30am


There is no textbook requirement for this course. Here are some suggestions for additional readings
LectureTopicAnnotated SlidesLabs
1 Probability reviewLecture 1Lists, Tuples and Dictionaries
2 Information theoryLecture 2Iterations and Error Handling
3 Data PreprocessingLecture 3Data Preprocessing
4 k-nearest neighborsLecture 4
5 Naive BayesLecture 5
6 Decision Trees and Random ForestsLecture 6Grid Search Cross-Validation
7 Support vector machinesLecture 7
8 AdaBoost and Gradient Boosting MachinesLecture 8SVMs and Boosted Trees
9 Clustering and Gaussian Mixture ModelsLecture 9HuggingFace Spaces
10 Neural NetworksLecture 10Intro to PyTorch
11 Convolutional neural networksLecture 11CNNs
12 Recurrent neural networksLecture 12LSTMs
13 TransformersLecture 13Transformers
14 Generative Adversarial Networks and Diffusion ModelsLecture 14DCGANs