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.

Lecture	Topic	Annotated Slides	Labs
1	Probability review	Lecture 1	Lists, Tuples and Dictionaries
2	Information theory	Lecture 2	Iterations and Error Handling
3	Data Preprocessing	Lecture 3	Data Preprocessing
4	k-nearest neighbors	Lecture 4
5	Naive Bayes	Lecture 5
6	Decision Trees and Random Forests	Lecture 6	Grid Search Cross-Validation
7	Support vector machines	Lecture 7
8	AdaBoost and Gradient Boosting Machines	Lecture 8	SVMs and Boosted Trees
9	Clustering and Gaussian Mixture Models	Lecture 9	HuggingFace Spaces
10	Neural Networks	Lecture 10	Intro to PyTorch
11	Convolutional neural networks	Lecture 11	CNNs
12	Recurrent neural networks	Lecture 12	LSTMs
13	Transformers	Lecture 13	Transformers
14	Generative Adversarial Networks and Diffusion Models	Lecture 14	DCGANs