229352 | Statistical Learning for Data Science 2

There is no textbook requirement for this course. Here are some suggestions for additional readings

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	Annotated Slides	Lab Notebook (Colab)
1	Probability review	Lecture 1	Lists, Tuples and Dictionaries
2	Information theory	Lecture 2	Iterations and Error Handling
3	Data Preprocessing	Lecture 3	Grid Search Cross-Validation
4	k-nearest neighbors	Lecture 4
5	Naive Bayes	Lecture 5
6	Decision Trees and Random Forests	Lecture 6	Decision Trees and RFs
7	Support vector machines	Lecture 7	Decision boundary of SVM
8	AdaBoost and Gradient Boosting Machines	Lecture 8	Boosted Trees
9	Clustering and Gaussian Mixture Models	Lecture 9	Deploy through Streamlit and HF
10	Neural Networks	Lecture 10	Intro to Tensors NNs in Pytorch
11	Convolutional neural networks	Lecture 11	Image classification
12	Recurrent neural networks	Lecture 12	Lyrics generation
13	Transformers	Lecture 13	Using Transformers library
14	Generative Adversarial Networks and Diffusion Models	Lecture 14	DCGANs and Diffusion model