Resources
Lecture Notes & Template
- Lecture 1: What Is Machine Learning?Download Lecture 1 PDF
- Lecture 2: k-Nearest NeighborsDownload Lecture 2 PDF
- Lecture 3: Linear RegressionDownload Lecture 3 PDF
- Lecture 4: Gradient Descent and OptimizationDownload Lecture 4 PDF
- Lecture 5: Probabilistic ClassificationDownload Lecture 5 PDF
- Lecture 6: Evaluation Pitfalls and Data VisualizationDownload Lecture 6 PDF
- Lecture 7: Regularization and GeneralizationDownload Lecture 7 PDF
- Lecture 8: Modern Decision TreesDownload Lecture 8 PDF
- Lecture 9: Ensemble MethodsDownload Lecture 9 PDF
- All lecture-note PDFs on GitHub
- Learn ML by Building LaTeX template — reusable source, figure and table conventions, and a compiled preview.
Software & Tools
Documentation
- NumPy Documentation
- PyTorch Tutorials
- fast.ai — inspiration for our practical, project-first teaching approach
- Scikit-learn User Guide