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Course Guidelines
About the course Prerequite Material References Extra Material Errata to Video Welcome
Python
Jupyter Notebooks Python overview
Exercises
Before semester start: Installation 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 2: Data analysis, manipulation and plotting Week 3: Linear algebra Week 4: Transformations tutorial Week 5: 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

Document

  • Overview

Content

Errata for video

As the course evolves, small updates may be introduced. Below is a list of changes where the course this year differ from the video:

  1. The symbols used for the exercises have been updated to:

    1. There will be 3 types of mandatory tasks:
      1. Mandatory Exercises
      2. Mandatory Assignment (larger)
      3. 1 Presentation of an exercise for each group (all members present).
      4. Enter 11 out of 14 weekly time distribution estimates.