## Syllabus and Class Schedule

DEEP LEARNING FOR COMPUTER VISION

COMS W 4995 006 (3 pts)

TR 02:40P-03:55P

Peter Belhumeur pb2019 C002442097

Cap: 50

www.deeplearningforcomputervision.com

COMS W 4995 006 (3 pts)

TR 02:40P-03:55P

Peter Belhumeur pb2019 C002442097

Cap: 50

www.deeplearningforcomputervision.com

Jan 16: Introduction to the Course (slides) (slides with voiceover)

Jan 18: Introduction to Computer Vision (slides) (slides with voiceover)

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) (jupyter notebook)

Feb 8: Catch-up Day

Feb 13: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)

Feb 14: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)(jupyter notebook)

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) (MNIST jupyter notebook) (CIFAR-10 jupyter notebook)

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) (Domain Transfer jupyter notebook)

Mar 29: Optimization (slides)

Apr 3: RNNs, LSTMs, and GRUs (slides) (LSTM Character Prediction jupyter notebook)

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)

Jan 18: Introduction to Computer Vision (slides) (slides with voiceover)

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) (jupyter notebook)

Feb 8: Catch-up Day

Feb 13: More on: Logistic Regression, Computational Graphs, Backpropagation (same slides as last class)

Feb 14: Linear Networks, Troubles with XOR, Perceptron, Hidden Layers, MLPs (slides)(jupyter notebook)

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) (MNIST jupyter notebook) (CIFAR-10 jupyter notebook)

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) (Domain Transfer jupyter notebook)

Mar 29: Optimization (slides)

Apr 3: RNNs, LSTMs, and GRUs (slides) (LSTM Character Prediction jupyter notebook)

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%**