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: 601 Sherman Fairchild
Cap: 60
www.deeplearningforcomputervision.com
COMS W 4995 006 (3 pts)
TR 02:40P-03:55P
Peter Belhumeur pb2019 C002442097
Location: 601 Sherman Fairchild
Cap: 60
www.deeplearningforcomputervision.com
Sep 3: Introduction to the Course (slides)
Sep 5: Introduction to Computer Vision (slides)
Sep 10: Introduction to Machine Learning (slides)
Sep 12: Probability, Bayes Theorem and Bayes Classification (slides)
Sep 17: Introduction to Python and Numpy (jupyter notebook)
Sep 19: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Sep 24: Logistic Regression, Computational Graphs, Backpropagation (slides)
Sep 26: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)
Oct 1: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 3: More on...Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 8: Universal Approximation Theorem, Multi-class Classification, Softmax (slides)
Oct 10: Regularization (same slides as last class)
Oct 15: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Oct 17: Working with Images and Convolution (slides)
Oct 22: Convolutional Neural Networks (CNNs) (same slides as last class)
Oct 24: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Oct 29: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Oct 31: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Nov 5: Optimization (slides)
Nov 7: NO CLASS
Nov 12: RNNs, LSTMs, and GRUs (slides)
Nov 14: Image Captioning (same slides as last class)
Nov 19: Autoencoders (slides)
Nov 21: Generative Advsersarial Networks (slides)
Nov 26: Discuss Final Projects and Pizza Day
Nov 28: Thanksgiving
Dec 3: Student Presentations
Dec 5: Student Presentations
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
Sep 5: Introduction to Computer Vision (slides)
Sep 10: Introduction to Machine Learning (slides)
Sep 12: Probability, Bayes Theorem and Bayes Classification (slides)
Sep 17: Introduction to Python and Numpy (jupyter notebook)
Sep 19: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Sep 24: Logistic Regression, Computational Graphs, Backpropagation (slides)
Sep 26: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)
Oct 1: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 3: More on...Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 8: Universal Approximation Theorem, Multi-class Classification, Softmax (slides)
Oct 10: Regularization (same slides as last class)
Oct 15: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Oct 17: Working with Images and Convolution (slides)
Oct 22: Convolutional Neural Networks (CNNs) (same slides as last class)
Oct 24: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Oct 29: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Oct 31: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Nov 5: Optimization (slides)
Nov 7: NO CLASS
Nov 12: RNNs, LSTMs, and GRUs (slides)
Nov 14: Image Captioning (same slides as last class)
Nov 19: Autoencoders (slides)
Nov 21: Generative Advsersarial Networks (slides)
Nov 26: Discuss Final Projects and Pizza Day
Nov 28: Thanksgiving
Dec 3: Student Presentations
Dec 5: Student Presentations
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
