Reading: Chapter 2, The Elements of Statistical Learning. Hastie, Friedman, Tibshirani.
Chapter 5, Deep Learning Book.
Participate in the general quiz please! (5 points for participation).
Assignment #1 available (due
Jan. 26 Jan. 31 4:00PM, submit in paper to TA by the time of the start of the class on Jan. 26 Jan. 31)
Reading: Chapter 4 and Chapter 6, Deep Learning Book
Thursday: Convolutional Neural Networks
Suggested readings: Sections 8.1 - 8.3 and Chapter 9, Deep Learning Book
VGG Convolutional Networks Practical, great hands-on tutorial of CNN.
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis P. Simard, D. Steinkraus and J.C. Platt. ICDAR 2003. About elastic deformation.
Chapter 5, Pattern Recognition and Machine Learning. Christopher Bishop. Springer, 2006.
Assignment #2 available (due Feb 13th 11:59PM, submit zip file on Canvas)
Tuesday: Convolutional Neural Networks
Thursday: Convolutional Neural Networks (cont.) (Visualization of Convolutional Networks)
Suggested readings: Zeiler and Fergus. Visualizing and Understanding Convolutional Networks. ECCV 2014
K. Simonyan, A. Vedaldi and A. Zisserman. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, ICLR 2014.
Tuesday: Convolutional Neural Networks (cont.) + Deconvolution and Other Vision Problemspoll for extra session time please!
Suggested readings: Goodfellow. NIPS 2016 Tutorial: Generative Adversarial Networks. arXiv:1701:00160
Tuesday: Recurrent Neural Models (Slides updated!)
Thursday: Project Proposal Presentations
Tuesday: Project Proposal Presentations + Tutorial on Theano
Thursday: Recurrent Neural Models (cont.) + Neural Network Optimization #2
Assignment #3 available on Canvas
February 22th - 26th
Tuesday: Recurrent Neural Models (cont.) + Neural Network Optimization #2
Thursday: Neural Network Optimization #2
Mar 6th - Mar 10th
Tuesday: Deep Learning in NLP (Guest lecture from Dr. Liang Huang)
Thursday: Neural Network Regularization
Mar 13th - Mar 17th
Tuesday: Deep Reinforcement Learning (Guest lecture from Alan Fern)
Thursday: Deep Reinforcement Learning (Guest lecture from Alan Fern)