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CS256Fall 2017Lecture Notes

Topics in Artificial Intelligence

Videos of lectures are available.

Below are my lecture notes for the class so far. They should serve as a rough guide to what was covered on any given day. Frequently, however, I say more in class than is in these notes. Also, I tend to dynamically correct typos on the board that might appear in these lecture notes. So caveat emptor.

Week 1: [Aug 25 - Syllabus]

Week 2: [Aug 28 - What is Deep Learning?] [Aug 30 - Probability, PAC Learning, Linear Algebra]

Week 3: [Sep 4 - Labor Day - No Class] [Sep 6 - Finish First-Pass Linear Algebra, Perceptrons].

Week 4: [Sep 11 - Perceptrons Learning, Python] [Sep 13 - Finish PAC result, Python]

Week 5: [Sep 18 - Finish Python First Pass] [Sep 20 - Perceptron Lower and Upper Bounds]

Week 6: [Sep 25 - Perceptron Networks and p-time algorithms, SVMs] [Sep 27 - SVM Training]

Week 7: [Oct 4 - Practice Midterm 1] [Oct 6 - Midterm 1]

Week 8: [Oct 9 - Numpy, Pillow] [Oct 11 - Neural Net Experiments, Feedforward Networks]

Week 9: [Oct 16 - Cost Functions, Output Layers] [Oct 18 - Minimization Methods, Hidden Units, Stochastic Gradient Descent]

Week 10: [Oct 23 - Backpropagation, Tensorflow] [Oct 23 - More Tensorflow, Regularization]

Week 11: [Oct 30 - Practice Midterm 2] [Nov 1 - Midterm 2]

Week 12: [Nov 6 - More Regularization] [Nov 8 - Finish Regularization, Optimization]

Week 13: [Nov 13 - Finish Optimization] [Nov 15 - CNNs and RNNs]

Week 14: [Nov 20 - Recurrent Neural Networks] [Nov 22 - Thanksgiving Break]

Week 15: [Nov 27 - Finish RNNs, Neural Network Design Methodology] [Nov 29 - NN Design Methodology]

Week 16: [Dec 4 - Finish NN Design, NN Applications] [Dec 6 - More NN Applications]