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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

  • Overview
  • 1. Basic Linear Algebra in Python
  • 2. Pen and paper exercises

Content

Linear Algebra

The focus of this exercise is on linear algebra using Python. There are both pen-and-paper exercises and coding exercises using Numpy. We believe that learning both in parallel gives you the best opportunities for understanding and applying essential linear algebra skills needed for the course.

Learning goals

After this week's exercises, you should be able to:

  • Reason about and apply matrix multiplication through Python.
  • Reason about and apply elementary operations (elementwise addition/multiplication, transpose, inverse, determinant) on matrices through Python.
  • Solve linear systems of equations in Python using the matrix inverse.
  • Notice that this week contains a mandatory exercise (look for the star in the header of each task, as shown below)