Mathematics for Machine Learning
The planned outline of 14 modules. Lessons are written, run and reviewed before they are published.
Before this track
Planned outline
- Module 1
Orientation, notation and the numerical toolkit
Coming soon - Module 2
Vectors and geometry
Coming soon - Module 3
Matrices and linear maps
Coming soon - Module 4
Linear systems, rank and subspaces
Coming soon - Module 5
Orthogonality, projections and least squares
Coming soon - Module 6
Eigenvalues and eigenvectors
Coming soon - Module 7
SVD, low-rank structure and PCA
Coming soon - Module 8
Single-variable calculus (GATE DA Section 3)
India-specific Coming soon - Module 9
Multivariable and matrix calculus
Coming soon - Module 10
Optimisation for machine learning
Coming soon - Module 11
Probability for machine learning
Coming soon - Module 12
Information theory for ML
Coming soon - Module 13
Maths inside modern neural networks
Coming soon - Module 14
GATE DA mathematics
India-specific Coming soon
Official documentation
- numpy.org/doc/stable (numpy.org)
- gate2027.iitm.ac.in/static/doc/GATE2027_Syllabus/DA_GATE2027_Syllabus.pdf (gate2027.iitm.ac.in)
- developers.google.com/machine-learning/crash-course (developers.google.com)
More in AI and machine learning
- Machine Learning (scikit-learn) (coming soon)
- AI Literacy & Prompting (coming soon)
- Building LLM Applications (coming soon)
- Artificial Intelligence (classical AI and soft computing) (coming soon)
- All AI & ML tracks
- How we make lessons