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Probabilistic Foundations of ML

Syllabus

Goals and Expectations
Frequently Asked Questions
Course Policies
Skills Check
Schedule
Office Hours
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Assignments

How to Download/Run/Submit
Homework 1
Homework 2
Homework 3
Homework 4
Homework 5
Homework 6
Homework 7

Introduction

1. Probabilistic ML: What is it? Why use it?
2. Introduction to Vectorization
3. Advanced Vectorization

Directed Graphical Models

4. Probability (Discrete)
5. Conditional Probability (Discrete)
6. Joint Probability (Discrete)
7. The Ethics of Data

Frequentist Learning

8. Maximum Likelihood: Theory
9. Maximum Likelihood: Code
10. Optimization
11. Probability (Continuous)
12. The Ethics of Learning from Data

Predictive Models

13. Regression
14. Classification
15. Neural Networks
16. Model Selection & Evaluation
17. The Ethics of Predictive Models in Sociotechnical Systems

Generative Models

18. Gaussian Mixture Models (Clustering)
19. Factor Analysis (Dimensionality Reduction)
20. The Ethics of Generative Models in Sociotechnical Systems

Bayesian Models

21. Priors and Posteriors
22. Posterior Predictives
23. The Ethics of Uncertainty and Interpretability in Human-AI Systems

Synthesis

24. The Ethics of Machine Learning: A View from History

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Probabilistic Foundations of Machine Learning by Yaniv Yacoby is licensed under CC BY-NC-SA 4.0