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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. Fitting polynomial models
  • 2. Pen and paper exercises

Content

Linear Transformations

This week's exercises introduce linear transformations and their implementation in Python.

Learning goals

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

  • Identify and reason about model parameters in a polynomial model.
  • Construct a design matrix.
  • Learn parameters for a polynomial model
  • Use the polynomial model to make predictions.
  • Visualize the predictions and reason about the performance of the model.
  • Notice that this week contains a mandatory exercise (look for the star in the header of each task, as shown below)