Introduction to Machine Learning
This documentation is designed to be an additional resource for students and a reliable, interactive knowledge base for anyone interested in machine learning (ML). It covers the content from the University of Alberta's courses:
- CMPUT 566 - Machine Learning Essentials, instructed by Professors Lili Mou;
- CMPUT 267 - Machine Learning I, for which I have served as a teaching assistant under Professor Dieter BΓΌchler.
Referencesβ
-
Stanford - CS229: Machine Learning (2023)
Lecture Notes (PDF) by Professors Andrew Ng and Tengyu Ma. -
University of Alberta - CMPUT 566: Machine Learning Essentials (2024)
Lecture Notes (PDF) by Professor Martha White. -
University of Alberta - CMPUT 504: Machine Learning II (2025)
Lecture Notes (PDF) by Professor Martha White.
ποΈ 1. Introduction to Machine Learning
In this chapter you'll be introduced to:
ποΈ 2. Supervised Learning
In this chapter you'll be introduced to:
ποΈ 3. Linear Regression
In this chapter you'll be introduced to:
ποΈ 4. Convexity
In this chapter you'll be introduced to:
ποΈ 5. Closed-Form Solution for Mean Squared Error
In this chapter you'll be introduced to:
ποΈ 6. Gradient-Based Optimization
In this chapter, you'll be introduced to:
ποΈ 7. Probabilistic Interpretation
In this chapter, you'll be introduced to:
ποΈ 8. Bias-Variance Tradeoff
In this chapter, you'll be introduced to:
ποΈ 9. Regularization
In this chapter, you'll learn about:
ποΈ 10. MAP Estimation and Hyperparameter Tuning
In this chapter, you'll learn about:
ποΈ 11. Bayesian Learning
In this chapter, you'll learn about:
ποΈ 12. Linear Classification
In this chapter, you'll learn about:
ποΈ 13. Logistic Regression
In this chapter, you'll learn about:
ποΈ 14. Softmax Regression
In this chapter, you'll learn about: