Syllabus and Class Schedule
DEEP LEARNING FOR COMPUTER VISION
COMS W 4995 006 (3 pts)
TR 02:40P-03:55P
Peter Belhumeur pb2019 C002442097
Location: 603 Hamilton
Cap: 50
www.deeplearningforcomputervision.com
COMS W 4995 006 (3 pts)
TR 02:40P-03:55P
Peter Belhumeur pb2019 C002442097
Location: 603 Hamilton
Cap: 50
www.deeplearningforcomputervision.com
(Jan 16: Introduction to the Course (slides)
Jan 18: Introduction to Computer Vision (slides)
Jan 23: Introduction to Machine Learning (slides)
Jan 25: Probability, Bayes Theorem and Bayes Classification (slides)
Jan 30: Introduction to Python and Numpy (jupyter notebook)
Feb 1: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Feb 6: Logistic Regression, Computational Graphs, Backpropagation (slides)
Feb 8: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)
Feb 13: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Feb 15: More on...Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Feb 20: Universal Approximation Theorem, Multi-class Classification, Softmax (slides)
Feb 22: Regularization (same slides as last class)
Feb 27: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Mar 1: Working with Images and Convolution (slides)
Mar 6: Convolutional Neural Networks (CNNs) (same slides as last class)
Mar 8: Final Project Discussions
Mar 13: Spring Break
Mar 15: Spring Break
Mar 20: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Mar 22: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Mar 27: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Mar 29: Optimization (slides)
Apr 3: RNNs, LSTMs, and GRUs (slides)
Apr 5: Catch-up Day
Apr 10: Image Captioning (same slides as last class)
Apr 12: Autoencoders (slides)
Apr 17: Generative Advsersarial Networks (slides)
Apr 19: Student Presentations (schedule)
Apr 24: Student Presentations (schedule)
Apr 26: Student Presentations (schedule)
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
Jan 18: Introduction to Computer Vision (slides)
Jan 23: Introduction to Machine Learning (slides)
Jan 25: Probability, Bayes Theorem and Bayes Classification (slides)
Jan 30: Introduction to Python and Numpy (jupyter notebook)
Feb 1: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Feb 6: Logistic Regression, Computational Graphs, Backpropagation (slides)
Feb 8: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)
Feb 13: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Feb 15: More on...Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Feb 20: Universal Approximation Theorem, Multi-class Classification, Softmax (slides)
Feb 22: Regularization (same slides as last class)
Feb 27: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Mar 1: Working with Images and Convolution (slides)
Mar 6: Convolutional Neural Networks (CNNs) (same slides as last class)
Mar 8: Final Project Discussions
Mar 13: Spring Break
Mar 15: Spring Break
Mar 20: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Mar 22: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Mar 27: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Mar 29: Optimization (slides)
Apr 3: RNNs, LSTMs, and GRUs (slides)
Apr 5: Catch-up Day
Apr 10: Image Captioning (same slides as last class)
Apr 12: Autoencoders (slides)
Apr 17: Generative Advsersarial Networks (slides)
Apr 19: Student Presentations (schedule)
Apr 24: Student Presentations (schedule)
Apr 26: Student Presentations (schedule)
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
