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 CMPUT 466/566 course by professor Lili Mou.
References
- Stanford CS229 - Lecture Notes by professors Andrew Ng and Tengyu Ma.
- University of Alberta CMPUT566 (Machine Learning Essentials) - Lecture Notes by professor Martha White.
- University of Alberta CMPUT504 (Machine Learning intermediate) - Lecture Notes by professor Martha White.
📄️ 1. Introduction to Machine Learning
In this chapter you'll be introduced to:
📄️ 2. Supervised Learning
In this chapter you'll be introduced to:
📄️ 3. Linear Regression
In this chapter you'll be introduced to:
📄️ 4. Convexity
In this chapter you'll be introduced to:
📄️ 5. Closed-Form Solution for Mean Squared Error
In this chapter you'll be introduced to:
📄️ 6. Gradient-Based Optimization
In this chapter, you'll be introduced to:
📄️ 7. Probabilistic Interpretation
In this chapter, you'll be introduced to:
📄️ 8. Bias-Variance Tradeoff
In this chapter, you'll be introduced to:
📄️ 9. Regularization
In this chapter, you'll learn about:
📄️ 10. MAP Estimation and Hyperparameter Tuning
In this chapter, you'll learn about:
📄️ 11. Bayesian Learning
In this chapter, you'll learn about:
📄️ 12. Linear Classification
In this chapter, you'll learn about:
📄️ 13. Logistic Regression
In this chapter, you'll learn about:
📄️ 14. Softmax Regression
In this chapter, you'll learn about: