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: 451 Computer Science Building
Cap: 100
www.deeplearningforcomputervision.com
COMS W 4995 006 (3 pts)
TR 02:40P-03:55P
Peter Belhumeur pb2019 C002442097
Location: 451 Computer Science Building
Cap: 100
www.deeplearningforcomputervision.com
Sep 8: Introduction to the Course (slides)
Sep 13: Introduction to Computer Vision (slides)
Sep 15: Introduction to Machine Learning (slides)
Sep 20: Probability, Bayes Theorem and Bayes Classification (slides)
Sep 22: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Sep 27: Logistic Regression, Computational Graphs, Backpropagation (slides)
Sep 29: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 4: More on: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides from last class)
Oct 6: Python and Numpy Tutorial
Oct 11: Universal Approximation Theorem, Multi-class Classification (slides)
Oct 13: Multi-class Classification, Softmax, Regularization (slides from last class)
Oct 18: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Oct 20: Working with Images and Convolution (slides)
Oct 25: Convolutional Neural Networks (CNNs) (same slides as last class)
Oct 27: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Nov 1: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Nov 4: Discuss Final Projects and Pizza Day
Nov 8: ELECTION DAY, NO CLASS
Nov 10: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Nov 15: Optimization (slides)
Nov 17: RNNs, LSTMs, GRUs, and Transformers (slides)
Nov 22: NO CLASS
Nov 24: THANKSGIVING, NO CLASS
Nov 29: Image Captioning (same slides as last class)
Dec 1: Autoencoders (slides) and Generative Adversarial Networks (slides)
Dec 6: Student Presentations
Dec 8: Student Presentations
Dec 9: Final Projects Due
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%
Sep 13: Introduction to Computer Vision (slides)
Sep 15: Introduction to Machine Learning (slides)
Sep 20: Probability, Bayes Theorem and Bayes Classification (slides)
Sep 22: High-Dim Feature Spaces, Curse of Dimensionality, Gradient Descent, SVMs (slides) (jupyter notebooks)
Sep 27: Logistic Regression, Computational Graphs, Backpropagation (slides)
Sep 29: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)
Oct 4: More on: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides from last class)
Oct 6: Python and Numpy Tutorial
Oct 11: Universal Approximation Theorem, Multi-class Classification (slides)
Oct 13: Multi-class Classification, Softmax, Regularization (slides from last class)
Oct 18: Early Stopping, Bagging, Ensemble Methods, Dropout, Data Augmentation (same slides as last class)
Oct 20: Working with Images and Convolution (slides)
Oct 25: Convolutional Neural Networks (CNNs) (same slides as last class)
Oct 27: An Introduction to Deep Learning Frameworks and Cloud Computing (slides)
Nov 1: A Quick History of Image Datasets, Big Datasets, Deeper Deep Nets (slides)
Nov 4: Discuss Final Projects and Pizza Day
Nov 8: ELECTION DAY, NO CLASS
Nov 10: Domain Transfer, Fine-Tuning Deep Nets, and DeepNet Regressors for Object Detection/Localization (slides)
Nov 15: Optimization (slides)
Nov 17: RNNs, LSTMs, GRUs, and Transformers (slides)
Nov 22: NO CLASS
Nov 24: THANKSGIVING, NO CLASS
Nov 29: Image Captioning (same slides as last class)
Dec 1: Autoencoders (slides) and Generative Adversarial Networks (slides)
Dec 6: Student Presentations
Dec 8: Student Presentations
Dec 9: Final Projects Due
Grading: Assignments = 60% + Final Project Proposal = 5% + Final Project = 35%