Course - Machine Learning MT23

Created: September 24, 2023 | Updated: October 21, 2025 | Read markdown | About these notes | View in context | Study these flashcards

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Covers lots of topics in machine learning from a very mathematical perspective: regression, regularisation, maximum likelihood estimates, optimisation, generative models, support vector machines, neural networks and backpropagation, CNNs, principal component analysis and clustering.

• Course Webpage
• Lecture Notes
• Practicals
  • Practical 1
  • Practical 2
  • Practical 3
• Overlaps with: Course - Numerical Analysis HT24^U
• The successor to the Prelims^U course: Course - Continuous Mathematics HT23^U
• Other courses this term: Courses MT23^U

Notes

• Notes - Machine Learning MT23, Basis expansion^U
• Notes - Machine Learning MT23, Bayesian machine learning^U
• Notes - Machine Learning MT23, Classification^U
• Notes - Machine Learning MT23, Clustering^U
• Notes - Machine Learning MT23, Convex optimisation^U
• Notes - Machine Learning MT23, Convolutional neural networks^U
• Notes - Machine Learning MT23, Covariance and correlation^U
• Notes - Machine Learning MT23, Cross-entropy loss^U
• Notes - Machine Learning MT23, Gaussian discriminant analysis^U
• Notes - Machine Learning MT23, Generative models^U
• Notes - Machine Learning MT23, Gradient descent^U
• Notes - Machine Learning MT23, Inverse transform sampling^U
• Notes - Machine Learning MT23, Kernels^U
• Notes - Machine Learning MT23, Learning curves^U
• Notes - Machine Learning MT23, Linear regression^U
• Notes - Machine Learning MT23, Logistic regression^U
• Notes - Machine Learning MT23, Matrix calculus^U
• Notes - Machine Learning MT23, Maximum likelihood principle^U
• Notes - Machine Learning MT23, Model selection^U
• Notes - Machine Learning MT23, Multivariate Gaussians^U
• Notes - Machine Learning MT23, Multidimensional scaling^U
• Notes - Machine Learning MT23, Naïve Bayes classifiers^U
• Notes - Machine Learning MT23, Neural networks^U
• Notes - Machine Learning MT23, Optimisation^U
• Notes - Machine Learning MT23, Paradigms^U
• Notes - Machine Learning MT23, Perceptrons^U
• Notes - Machine Learning MT23, Principal component analysis^U
• Notes - Machine Learning MT23, Recurrent neural networks^U
• Notes - Machine Learning MT23, Singular value decomposition^U
• Notes - Machine Learning MT23, Spectral clustering^U
• Notes - Machine Learning MT23, Support vector machines^U
• Notes - Machine Learning MT23, Overfitting and underfitting^U
• Notes - Machine Learning MT23, k-means clustering^U
• Notes - Machine Learning MT23, k-nearest neighbours^U
• Notes - Machine Learning MT23, Misc^U

Problem Sheets

• Sheet 0
• Sheet 1
• Sheet 2
• Sheet 3
• Sheet 4

Lectures

• Lecture - Machine Learning MT23, I^U
• Lecture - Machine Learning MT23, II^U
• Lecture - Machine Learning MT23, III^U
• Lecture - Machine Learning MT23, IV^U
• Lecture - Machine Learning MT23, V^U
• Lecture - Machine Learning MT23, VI^U
• Lecture - Machine Learning MT23, VII^U
• Lecture - Machine Learning MT23, VIII^U
• Lecture - Machine Learning MT23, IX^U
• Lecture - Machine Learning MT23, X^U
• Lecture - Machine Learning MT23, XI^U
• Lecture - Machine Learning MT23, XII^U
• Lecture - Machine Learning MT23, XIII^U
• Lecture - Machine Learning MT23, XIV^U
• Lecture - Machine Learning MT23, XV^U
• Lecture - Machine Learning MT23, XVI^U
• Lecture - Machine Learning MT23, XVII^U
• Lecture - Machine Learning MT23, XVIII^U
• Lecture - Machine Learning MT23, XIX^U
• Lecture - Machine Learning MT23, XX^U

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