README.md

Statistical Machine Learning 1RT700

This repository is used to host the files needed for the exercise sessions and the computer lab in the course Statistical Machine Learning at Uppsala University.

Problem solving sessions

The material associated with each session is given below together with a set of recommended problems.

For each session, the material consists of the following:

Pen-and-paper class:

• Single pdf file with problems and solutions.

Computer class:

• Jupyter notebook with problems.
• Direct link to run notebook in Google Colab.
• Notebook exported to HTML with solutions.

Data used in the computer classes can be downloaded directly in the notebooks. For offline use, we recommend you download the whole repository and make the necessary changes to the notebook by commenting/uncommenting appropriate lines.

	Topic	Type	Recommended	Extra	Files
1	Linear regression	Pen-and-paper	1, 2, 3	4, 5
2	Linear regression	Computer	1, 2, 3, 4	5
3	Logistic regression, LDA, QDA, kNN	Pen-and-paper	1, 2, 3, 5	4, 6, 7
4	Logistic regression, LDA, QDA, kNN	Computer	1, 2, 3, 4	5
5	Bias and variance, model selection, cross validation	Pen-and-paper	1, 2, 3, 4	5, 6
6	Bias and variance, model selection, cross validation	Computer	1, 2, 3	4
7	Tree-based methods	Pen-and-paper	1, 2, 3, 4
8	Tree-based methods	Computer	1, 2	3
9	Boosting	Computer	1, 2, 3	4, 5
10	Neural networks	Pen-and-paper	1, 2, 3, 4

Computer lab

For the computer lab about deep learning the following resources are available:

Topic	File	Links
Lab instructions	instructions.pdf
Introduction to PyTorch	introduction.ipynb
One-layer NN for MNIST	mnist_onelayer.ipynb
Image classification with ResNet	ResNet_classification.ipynb
Multi-modal models	CLIP.ipynb