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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

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. Data wrangling and statistics
2. Cross validation tutorial with Scikit Learn

Content

Statistics and Cross validation

The following tutorials are focused on descriptive statistics and on a core concept within machine learning: cross-validation.