6 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019) @riskone1
6 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019) @riskone1
Uploaded February 2019 | Updated September 2026, 3 days ago
NB: There is a mistake in slide 59. It should be max(0, 1 - y^i(w^T\x + b) ) (one minus the error instead of the other way around).

slides: mlvu.github.io/lectures/32.LinearModels2.annotated.pdf
course materials: mlvu.github.io

Today we discuss the two most popular machine learning models of the 90s: neural networks, and support vector machines. We explain how they each work, and why they were each popular at specific times.
6 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 8.2: Maximum likelihood estimators10 Tree Models and Ensembles: Decision Trees, AdaBoost, Gradient Boosting (MLVU2019)MLVU 10.4: Boosting: Adaboost and gradient boostingMLVU 5.5: Information theory
MLVU |

6 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019)

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