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The maths engine room: vectors, matrices, calculus, optimisation, probability.
| Week | Lesson | Shows up in | Real practice |
|---|---|---|---|
| 1 | What is machine learning, and which maths do we need? Introduction to machine learning | Quiz 1 | 16 real questions |
| 2 | Slopes, change and the steepest way up Calculus | Quiz 1 | 38 real questions |
| 3 | Vectors, matrices and the best-fit line Linear Algebra - Least Squares Regression | Quiz 1 | 36 real questions |
| 4 | The special directions of a matrix Linear Algebra - Eigenvalues and eigenvectors | Quiz 1 | 55 real questions |
| 5 | Symmetric matrices: the well-behaved ones Linear Algebra - Symmetric matrices | Quiz 2 | 50 real questions |
| 6 | SVD and PCA: squeezing data Linear Algebra - Singular value decomposition, Principal Component Analysis in Image Processing | Quiz 2 | 28 real questions |
| 7 | Finding the lowest point: gradient descent Unconstrained Optimisation | Quiz 2 | 14 real questions |
| 8 | Bowls, not bumpy hills: convexity Convex sets, functions, and optimisation problems | Quiz 2 | 13 real questions |
| 9 | Optimising with rules: Lagrange and KKT Constrained Optimisation and Lagrange Multipliers. Logistic regression as an optimization problem | End Term | 23 real questions |
| 10 | Probability for machine learning Examples of probabilistic models in machine learning problems | End Term | 45 real questions |
| 11 | The exponential family Exponential Family of distributions | End Term | 2 real questions |
| 12 | Estimating parameters, and EM Parameter estimation. Expectation Maximization | End Term | 6 real questions |
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