Course Materials by Category
Basics & Foundations
Fundamental concepts and prerequisites for machine learning.
Mathematical Foundations
Core mathematical concepts underlying machine learning algorithms.
Optimization Algorithms
Techniques for finding optimal solutions in machine learning.
Supervised Learning
Algorithms that learn from labeled data.
- Decision Trees
- Bias-Variance Tradeoff
- Bias-Variance II
- Decision Tree Pruning
- Cross-Validation
- Ensemble Methods
- Linear Regression
- Logistic Regression
- K-Nearest Neighbors
- KNN Approximation Methods
- Naive Bayes
- Bayesian Networks
- Ridge Regression
- Lasso Regression
- Movie Recommendation Systems
- Feature Selection
- SVM Introduction
- SVM Soft Margin
- SVM Kernel Methods
- More SVM Topics
Neural Networks & Deep Learning
Modern neural network architectures and deep learning techniques.
Advanced Topics
Specialized techniques and cutting-edge approaches.
Unsupervised Learning
Algorithms that find patterns in unlabeled data.
How to Use These Materials
- For Students: Navigate through topics sequentially, starting with Basics & Foundations
- For Instructors: Each category is self-contained and can be taught independently
- PDF Quality: All slides are optimized for both viewing and printing
Quick Links
- Interactive Notebooks - Hands-on coding examples
- Course Homepage - Return to main page
- Instructor - Nipun Batra’s homepage