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Provider: ETH Zürich


This online lecture series presents the theory of fundamental machine learning concepts

Topics covered in the video lectures include:

  • Bayesian theory of optimal decisions
  • Maximum likelihood and Bayesian parameter inference
  • Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
  • Ensemble methods: Bagging and Boosting
  • Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
  • Non parametric density estimation: Parzen windows, nearest neighbour
  • Dimension reduction: principal component analysis (PCA) and beyond

(Also check out this list of various online courses on the topic of AI.)

ETH Zürich

Teacher: Buhmann Joachim Maximilian
Provided by: ETH Zürich
Course language: English
Fees: free course
Level: beginner
Certificate possible?
Format: video lecture

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