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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