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Course Guidelines
About the course Prerequite Material References
Python
Jupyter Notebooks Python overview
Exercises
Before the semester start: Installation and exercise setup Week 1: Introduction to Python and libraries Week 2: Vector representations Week 3: Linear Algebra Week 4: Linear Transformations Week 5: Models and least squares Week 6: Assignment 1 - Gaze Estimation Week 7: Model selection and descriptive statistics Week 8: Filtering Week 9: Classification Week 10: Evaluation Week 11: Dimensionality reduction Week 12: Clustering and refresh on gradients Week 13: Neural Networks Week 14: Convolutional Neural Networks (CNN's)
Tutorials
Week 1: Data analysis, manipulation and plotting Week 2: Linear algebra Week 3: Transformations tutorial Week 4: Projection and Least Squares tutorial Week 7: Cross-validation and descriptive statistics tutorial Week 8: Filtering tutorial Week 11: Gradient Descent / Ascent
In-class Exercises
In-class 1 In-class 2 In-class 10 In-class 3 In-class 4 In-class 8
Explorer

Document

Content

Material repository

Most exercises are given in Jupyter Notebooks. These are available from the materials folder in LearnIT

Two notebook versions are provided in separate folders: one for Jupyter Lab users and one for VSCode users (also compatible with PyCharm, Dataspell, and other editors).

We recommend using Jupyter Lab because it provides notebooks that closely resemble the webpage. However, the VSCode notebooks contain the same content but without formatting.

Each folder contains sub-folders that mirror the webpage urls. For example, the first exercise is placed in <rootDir>/<vscode|jupyter>/02-material/W1-introduction/ .