Event TypeDateDescriptionCourse Materials Lecture Jan 4 Intro to Computer Vision, historical context.
[slides] [video] 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 Lecture Jan 13 Backpropagation
Introduction to neural networks Lecture Jan 18 Holiday; No class. A1 Due Jan 20 Assignment #1 (kNN/SVM/Softmax) Due date
[Assignment #1] Lecture Jan 20 Getting Neural Networks to work: cross-validation process, optimization, debugging Lecture Jan 25 Convolutional Neural Networks: architectures, convolution / pooling layers Lecture Jan 27 Understanding and visualizing Convolutional Neural Networks Proposal due Jan 30 Couse Project Proposal due
[proposal description] Lecture Feb 1 What makes ConvNets tick
Transfer learning Lecture Feb 3 Squeezing out the last few percent
Training ConvNets in practice A2 Due Feb 3 Assignment #2 (Neural Nets) Due date Lecture Feb 8 Image captioning
Recurrent Neural Networks
RNN language models Midterm Feb 10 In-class midterm Lecture Feb 15 Holiday; No class. Milestone
Feb 17 Course Project Milestone Lecture Feb 17 ConvNets for spatial localization
Segmentation
Object detection A3 Due Feb 22 Assignment #3 (ConvNets) Due date Lecture Feb 22 Generating images with ConvNets
Deep dream
Artistic style transfer
Generative models: VAE / GAN Lecture Feb 24 ConvNets in full-stack agents
Reinforcement Learning overview
Deep Q Learning for ATARI game playing Lecture Feb 29 Training ConvNets in practice
Caffe tutorial
Overview of Torch/TensorFlow Lecture Mar 2 Attention Models
Spatial Transformer Networks Lecture Mar 7 Student spotlight talks Poster Presentation Mar 9 Mandatory! Details TBA Final Project Due
Mar 13 Final course project due date
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