Lecturer: Yossi Keshet

Teaching assistant: Yosi Shrem and Yael Segal

Shai Shalev-Shwartz and Shai Ben-David, "Understanding Machine Learning: From Theory to Algorithms", Cambridge University Press, 2014

Ian Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", MIT Press, 2016.

Date | Lecture | Recitation |
---|---|---|

27/20/2019 | Introduction | - |

03/11/2019 | Working intuitively: ERM principle, Optimal Bayes | Introduction, kNN, k-means |

10/11/2019 | Basic concepts: PAC learning, Finite hypotheses sets | ERM Principle example: the Perceptron Algorithm |

17/11/2019 | How to make it work better: VC, stability, and regularization | Be Practicle: binary and multiclass classification, Optimization (GD, SGD), code and visualization |

24/11/2019 | The large margin concept: Support Vector Machine (SVM) | The large margin concept: Online algorithms, linear regression |

01/12/2019 | How to turn linear models to handle non-linear data: SVM and kernels | Evaluation metrics, Logistic regression binary |

08/12/2019 | Intro to Neural Nets and the maximum likelihood estimator | Canceled |

15/12/2019 | The Backpropagation: training deep neural nets | The Backpropagation: training deep neural nets |

22/12/2019 | Programming: PyTorch | optimization methods (momentum, adagrad, adam) + tricks (do, batchnorm) |

29/12/2019 | Hanukkah vacation | Hanukkah vacation |

05/01/2020 | Learning on different scales: Convolutional Neural Nets (CNN) | CNN architectures (ResNet, skip-connections, ...) |

12/01/2020 | Working with sequential data: Recurrent Neural Nets (RNN) | RNN architectures (vanila, GRU, and LSTM) |

19/01/2020 | - | - |

26/01/2020 | - | - |

Lecture 1 - Introduction

Lecture 2 - ERM principle and Perceptron

Lecture 3 - The optimal Bayes classifier and Maximum likelihood estimator

Lecture 4 - PAC learnability and Support Vector Machines

Lecture 5 - Byond ERM: regularization, SRM, and MDL

Lecture 6 - introduction to deep learning. See Ch.4 of Deng

Lecture 7 - Convolutional neural networks (CNN) - slides are from Sandford CS231n course

A discussion group for this course is available on Piazza Q&A.

The submission should be using the Submit system