Stanford University CS231n: Convolutional Neural Networks for Visual Recognition

CS231n: Convolutional Neural Networks for Visual Recognition

Schedule and Syllabus

Unless otherwise specified the course lectures and meeting times are:

Monday, Wednesday 2:15-3:30
Bishop Auditorium in Lathrop Building (map)

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]