Algorithms and Mathematics
Mathematical foundations and algorithms underpin all of machine learning and artificial intelligence. A solid understanding of these concepts enables deeper insight into how models learn and make predictions.1
Topics in This Section
| Topic | Description |
|---|---|
| Linear Algebra Essentials | Vectors, matrices, eigenvalues, SVD |
| Probability & Statistics | Distributions, Bayes theorem, hypothesis testing |
| Calculus for ML | Gradients, chain rule, optimization |
| Graph Algorithms | Community detection, network analysis |
| Clustering Algorithms | K-means, Louvain, Leiden, hierarchical clustering |
| Information Theory | Entropy, KL divergence, mutual information |
Learning Path
Linear Algebra → Calculus for ML → Probability & Statistics → Information Theory
Related Domains
- Apply to neural networks? See 01 - AI Fundamentals
- Graph-based ML? See Community Detection
- Understanding loss functions? See Loss Functions
References
Strang, G. (2019). Linear Algebra and Learning from Data. Wellesley-Cambridge Press. https://math.mit.edu/~gs/learningfromdata/ ↩︎