1 Introduction to Machine Learning (MLVU2020) @riskone1
1 Introduction to Machine Learning (MLVU2020) @riskone1
Uploaded February 2020 | Updated September 2026, 5 days ago
slides: mlvu.github.io/lectures/11.Introduction.annotated.pdf
course materials: mlvu.github.io

The first lecture in the 2020 Machine learning course at the Vrije Universiteit Amsterdam. Today, we discuss the basic abstract tasks of offline machine learning: classification, regression, clustering, density estimation, and generative modeling. Lecturer: Peter Bloem.
1 Introduction to Machine Learning (MLVU2020)8 Probability 2: Maximum Likelihood, Gaussian Mixture Models and Expectation Maximization (MLVU2019)MLVU 5.3: The (naive) Bayes classfierMLVU 13.5: Social impact 46 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019)MLVU 3.6: No free lunch11 Sequential Data: Markov Models, Word Embeddings and LSTMsMLVU 3.1: Machine learning experimentsMLVU 9.4: Variational autoencoders (VAEs)MLVU 9.1: Generator networks06 Deep Learning 1: Neural networks, Convolutional layers (MLVU2018)5 Probability 1: Logistic regression, Log loss, Entropy (MLVU2020)
MLVU |

1 Introduction to Machine Learning (MLVU2020)

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