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: 413 Kent Hall
Cap: 60
www.deeplearningforcomputervision.com
COMS W 4995 004 (3 pts)
TR 02:40P-03:55P
Peter Belhumeur pb2019 C002442097
Location: 413 Kent Hall
Cap: 60
www.deeplearningforcomputervision.com
Sep 9: Introduction to the Course (slides)
Sep 14: Introduction to Computer Vision (slides)
Sep 16: Introduction to Machine Learning (slides)
Sep 21: Probability, Bayes Theorem and Bayes Classification (slides)
Sep 23: Introduction to Python and Numpy (jupyter notebook)
Sep 28: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Sep 30: Logistic Regression, Computational Graphs, Backpropagation (slides)
Oct 5: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)
Oct 7: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 12: More on...Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 14: Universal Approximation Theorem, Multi-class Classification, Softmax (slides)
Oct 19: Regularization (same slides as last class)
Oct 21: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Oct 26: Working with Images and Convolution (slides)
Oct 28: Convolutional Neural Networks (CNNs) (same slides as last class)
Nov 2: NO CLASS
Nov 4: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Nov 9: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Nov 11: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Nov 16: Optimization (slides)
Nov 18: RNNs, LSTMs, GRUs, and Transformers (slides)
Nov 23: Discuss Final Projects and Pizza Day
Nov 25: NO CLASS
Nov 30: Image Captioning (same slides as last class)
Dec 2: Autoencoders (slides) and Generative Advsersarial Networks (slides)
Dec 7: Student Presentations
Dec 9: Student Presentations
Dec 14: Final Projects Due
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
Sep 14: Introduction to Computer Vision (slides)
Sep 16: Introduction to Machine Learning (slides)
Sep 21: Probability, Bayes Theorem and Bayes Classification (slides)
Sep 23: Introduction to Python and Numpy (jupyter notebook)
Sep 28: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Sep 30: Logistic Regression, Computational Graphs, Backpropagation (slides)
Oct 5: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)
Oct 7: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 12: More on...Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 14: Universal Approximation Theorem, Multi-class Classification, Softmax (slides)
Oct 19: Regularization (same slides as last class)
Oct 21: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Oct 26: Working with Images and Convolution (slides)
Oct 28: Convolutional Neural Networks (CNNs) (same slides as last class)
Nov 2: NO CLASS
Nov 4: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Nov 9: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Nov 11: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Nov 16: Optimization (slides)
Nov 18: RNNs, LSTMs, GRUs, and Transformers (slides)
Nov 23: Discuss Final Projects and Pizza Day
Nov 25: NO CLASS
Nov 30: Image Captioning (same slides as last class)
Dec 2: Autoencoders (slides) and Generative Advsersarial Networks (slides)
Dec 7: Student Presentations
Dec 9: Student Presentations
Dec 14: Final Projects Due
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
