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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

Content

This tab contains critical details about expectations, hand-in format, recommendations, and prerequisite / refresh materials for the Introduction to Machine Learning course. Furthermore, it also has the list of references to reading materials, some of which can be downloaded from the materials folder in LearnIT. It is important that you carefully acquaint yourself with this contents before the semester starts.