📘 Mathematics for Machine Learning
Curated topics every AI/ML engineer must know, with priority tags to guide your learning.
- Scalars, Vectors, Matrices, Tensors✅ Must Know Deeply
- Matrix Operations: Addition, Subtraction, Multiplication✅ Must Know Deeply
- Matrix Transpose, Identity, and Inverse✅ Must Know Deeply
- Determinant, Rank, Trace✅ Must Know Deeply
- Matrix Indexing and Slicing (NumPy)✅ Must Know Deeply
- Dot Product, Cross Product, Vector Norms (L1, L2)✅ Must Know Deeply
- Distance Metrics: Euclidean, Manhattan, Cosine Similarity✅ Must Know Deeply
- Orthonormal Vectors and Gram-Schmidt Process✅ Must Know Deeply
- Linear Independence, Basis, and Dimension✅ Must Know Deeply
- Linear Transformations: Geometric Interpretation✅ Must Know Deeply
- Eigenvalues and Eigenvectors✅ Must Know Deeply
- Singular Value Decomposition (SVD)✅ Must Know Deeply
- Principal Component Analysis (PCA)✅ Must Know Deeply
- Applications: Dimensionality Reduction, Similarity Search, Data Compression✅ Must Know Deeply
- Change of Basis🔍 Know High-Level
- LU Decomposition🔍 Know High-Level
- Advanced Vector Space Theory🔍 Know High-Level
- Formal proofs of vector space theorems❌ Skippable