Event Type | Date | Description | Course Materials | |
---|---|---|---|---|
Lecture | Jan 5 | Intro to Computer Vision | [python tutorial] [slides] | |
Lecture | Jan 7 | Image classification, data-driven approach, k-nearest neighbor | [slides] [notes] | |
Lecture | Jan 12 | Linear classification: SVM/Softmax | [slides] [notes] [web demo] | |
Lecture | Jan 14 | Optimization, higher-level representations, image features | [slides] [notes] | |
A1 Due | Jan 21 | Assignment #1 (kNN/SVM/Softmax) Due date | [assignment #1] | |
Lecture | Jan 21 | Introduction to Neural Networks, backpropagation | [backprop notes] [neural net intro notes 1/3] [slides] |
|
Lecture | Jan 26 | Getting Neural Networks to work: cross-validation process, optimization, debugging |
[slides] [neural net notes part 2/3] [neural net notes part 3/3] |
|
Lecture | Jan 28 | Convolutional Neural Networks: architectures, convolution / pooling layers | [slides] [notes] | |
Proposal due | Jan 30 | Couse Project Proposal due | [proposal description] | |
Lecture | Feb 2 | Understanding and visualizing Convolutional Neural Networks | [slides] | |
Lecture | Feb 4 | What makes ConvNets tick, Transfer Learning | [slides] [notes] | |
A2 Due | Feb 6 | Assignment #2 (Neural Net / ConvNet) Due date | [assignment #2] | |
Lecture | Feb 9 | Squeezing out the last few percent, Training ConvNets in practice | [slides] | |
Midterm | Feb 11 | In-class midterm | ||
Milestone | Feb 16 | Course Project Milestone | ||
Lecture | Feb 18 | Beyond Image Classification: localization, detection, segmentation.
Recurrent Networks I: Image Captioning example |
[slides] | |
A3 Due | Feb 23 | Assignment #3 Due date | [assignment description] | |
Lecture | Feb 23 | Invited Speaker: Evan Shelhamer: Working with Caffe, an open-source ConvNet library | [slides] | |
Lecture | Feb 25 | Invited Speaker: Lubomir Bourdev, Facebook AI Research | ||
Lecture | Mar 2 | Invited Speaker: Jon Shlens, Google Brain | [slides] | |
Lecture | Mar 4 | Working with Caffe: hands-on tutorial with Justin |
[slides]
[code]
[coco_animals (3.9GB)] |
|
Lecture | Mar 9 | Mystery talk, Tiny ImageNet student spotlights, Recurrent Networks II, Attention Models |
[slides] [Tiny ImageNet leaderboard] |
|
Poster Presentation | Mar 11 | 2-5pm at Gates (AT&T patio) | ||
Final Project Due | Mar 15 | Final course project due date (due date moved: original was March 8) | [project description] |