Course - Machine Learning MT23

Created: September 24, 2023
Updated: November 20, 2024

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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 backpropogation, 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, Misc]]^U
• [[Notes - Machine Learning MT23, Model selection]]^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, Underfitting and overfitting]]^U
• [[Notes - Machine Learning MT23, k-means clustering]]^U
• [[Notes - Machine Learning MT23, k-nearest neighbours]]^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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Related posts

• [[Notes - Machine Learning MT23, Bayesian machine learning]]^U

  (incoming)
• [[Notes - Machine Learning MT23, k-means clustering]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Classification]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Misc]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Basis expansion]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Paradigms]]^U

  (incoming)
• [[Courses MT23]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Recurrent neural networks]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Maximum likelihood principle]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Convolutional neural networks]]^U

  (incoming)
• [[University Notes]]^U

  (incoming)
• [[Course - Numerical Analysis HT24]]^U

  (incoming)
• [[Course - Continuous Mathematics HT23]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Underfitting and overfitting]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Naïve Bayes classifiers]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Model selection]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Linear regression]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Support vector machines]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Singular value decomposition]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Perceptrons]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Covariance and correlation]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Convex optimisation]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Multidimensional scaling]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Generative models]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Gradient descent]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Inverse transform sampling]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Optimisation]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Neural networks]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Kernels]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Spectral clustering]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Gaussian discriminant analysis]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Learning curves]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Logistic regression]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Matrix calculus]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Clustering]]^U

  (incoming)
• [[Notes - Machine Learning MT23, k-nearest neighbours]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Cross-entropy loss]]^U

  (incoming)
• [[Notes - Machine Learning MT23, Principal component analysis]]^U

  (incoming)
• [[Lecture - Machine Learning MT23, IX]]^U

  (sim: 0.458)
• [[Lecture - Machine Learning MT23, III]]^U

  (sim: 0.457)
• [[Lecture - Machine Learning MT23, XII]]^U

  (sim: 0.452)