Syllabus and Class Schedule
DEEP LEARNING FOR COMPUTER VISION
COMS W 4995 004 (3 pts)
TR 02:40P-03:55P
Peter Belhumeur pb2019 C002442097
Location: Zoom
Cap: 60
www.deeplearningforcomputervision.com
COMS W 4995 004 (3 pts)
TR 02:40P-03:55P
Peter Belhumeur pb2019 C002442097
Location: Zoom
Cap: 60
www.deeplearningforcomputervision.com
Sep 8: Introduction to the Course (slides)
Sep 10: Introduction to Computer Vision (slides)
Sep 15: Introduction to Machine Learning (slides)
Sep 17: Probability, Bayes Theorem and Bayes Classification (slides)
Sep 22: Introduction to Python and Numpy (jupyter notebook)
Sep 24: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Sep 29: Logistic Regression, Computational Graphs, Backpropagation (slides)
Oct 1: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)
Oct 6: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 8: More on...Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 13: Universal Approximation Theorem, Multi-class Classification, Softmax (slides)
Oct 15: Regularization (same slides as last class)
Oct 20: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Oct 22: Working with Images and Convolution (slides)
Oct 27: Convolutional Neural Networks (CNNs) (same slides as last class)
Oct 29: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Nov 3: NO CLASS
Nov 5: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Nov 10: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Nov 12: Optimization (slides)
Nov 17: RNNs, LSTMs, and GRUs (slides)
Nov 19: Image Captioning (same slides as last class)
Nov 24: Discuss Final Projects and Pizza Day
Nov 26: NO CLASS
Dec 1: Autoencoders (slides)
Dec 3: Generative Advsersarial Networks (slides)
Dec 8: Student Presentations
Dec 10: Student Presentations
Dec 17: Final Projects Due
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
Sep 10: Introduction to Computer Vision (slides)
Sep 15: Introduction to Machine Learning (slides)
Sep 17: Probability, Bayes Theorem and Bayes Classification (slides)
Sep 22: Introduction to Python and Numpy (jupyter notebook)
Sep 24: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Sep 29: Logistic Regression, Computational Graphs, Backpropagation (slides)
Oct 1: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)
Oct 6: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 8: More on...Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 13: Universal Approximation Theorem, Multi-class Classification, Softmax (slides)
Oct 15: Regularization (same slides as last class)
Oct 20: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Oct 22: Working with Images and Convolution (slides)
Oct 27: Convolutional Neural Networks (CNNs) (same slides as last class)
Oct 29: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Nov 3: NO CLASS
Nov 5: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Nov 10: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Nov 12: Optimization (slides)
Nov 17: RNNs, LSTMs, and GRUs (slides)
Nov 19: Image Captioning (same slides as last class)
Nov 24: Discuss Final Projects and Pizza Day
Nov 26: NO CLASS
Dec 1: Autoencoders (slides)
Dec 3: Generative Advsersarial Networks (slides)
Dec 8: Student Presentations
Dec 10: Student Presentations
Dec 17: Final Projects Due
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
