Unless otherwise specified the course lectures and meeting times are Monday, Wednesday 3:00-4:20, Bishop Auditorium in Lathrop Building (map)

Event Type | Date | Description | Course Materials |
---|---|---|---|

Lecture | Jan 4 | Intro to Computer Vision, historical context. | [slides] |

Lecture | Jan 6 | Image classification and the data-driven approach k-nearest neighbor Linear classification I |
[slides] [video]
[python/numpy tutorial] [image classification notes] [linear classification notes] |

Lecture | Jan 11 |
Linear classification II Higher-level representations, image features Optimization, stochastic gradient descent |
[slides] [video]
[linear classification notes] [optimization notes] |

Lecture | Jan 13 | Backpropagation Introduction to neural networks |
[slides] [video]
[backprop notes] [Efficient BackProp] (optional) related: [1], [2], [3] (optional) |

Lecture | Jan 18 | Holiday; No class. | |

A1 Due | Jan 20 | Assignment #1 (kNN/SVM/Softmax/2-Layer Net) Due date | [Assignment #1] |

Lecture | Jan 20 | Training Neural Networks Part 1 activation functions, weight initialization, gradient flow, batch normalization babysitting the learning process, hyperparameter optimization |
[slides] [video]
Neural Nets notes 1 Neural Nets notes 2 Neural Nets notes 3 tips/tricks: [1], [2], [3] (optional) Deep Learning [Nature] (optional) |

Lecture | Jan 25 |
Training Neural Networks Part 2: parameter updates, ensembles, dropout Convolutional Neural Networks: intro |
[slides] [video]
Neural Nets notes 3 |

Lecture | Jan 27 |
Convolutional Neural Networks: architectures, convolution / pooling layers Case study of ImageNet challenge winning ConvNets |
[slides] [video]
ConvNet notes |

Proposal due | Jan 30 | Couse Project Proposal due | [proposal description] |

Lecture | Feb 1 |
ConvNets for spatial localization Object detection |
[slides] [video] |

Lecture | Feb 3 |
Understanding and visualizing Convolutional Neural Networks Backprop into image: Visualizations, deep dream, artistic style transfer Adversarial fooling examples |
[slides] [video] |

A2 Due | Feb 5 | Assignment #2 (Neural Nets) Due date | [Assignment #2] |

Lecture | Feb 8 |
Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) RNN language models Image captioning |
[slides] [video]
DL book RNN chapter (optional) min-char-rnn, char-rnn, neuraltalk2 |

Midterm | Feb 10 | In-class midterm | |

Lecture | Feb 15 | Holiday; No class. | |

Milestone | Feb 17 |
Course Project Milestone | |

Lecture | Feb 17 |
Training ConvNets in practice Data augmentation, transfer learning Distributed training, CPU/GPU bottlenecks Efficient convolutions |
[slides] [video] |

Lecture | Feb 22 | Overview of Caffe/Torch/Theano/TensorFlow | [slides] [video] |

A3 Due | Feb 24 | Assignment #3 (ConvNets) Due date | [Assignment #3] |

Lecture | Feb 24 |
Segmentation Soft attention models Spatial transformer networks |
[slides] [video] |

Lecture | Feb 29 |
ConvNets for videos Unsupervised learning |
[slides] [video] |

Lecture | Mar 2 | Invited Talk: Jeff Dean [video] | |

Lecture | Mar 7 | Student spotlight talks, conclusions | [slides] |

Poster Presentation | Mar 9 | ||

Final Project Due | Mar 13 |
Final course project due date | [reports] |