Chris Pollett> CS256
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CS256 Fall 2021 Lecture 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 23 What is Deep Learning?] [Aug 25 Defining Learning - Introduction to Probability]

Week 2: [Aug 30 Intro Probability and Linear Algebra - PAC Learning] [Sep 1 Finish Intro to Linear Algebra - Perceptrons]

Week 3: [Sep 6 - Labor Day] [Sep 8 Perceptron Learning]

Week 4: [Sep 13 PAC-Learning Gradual Thresholds - Python][ Sep 13 Finish First Pass Python - Go/No-Go Perceptron Results]

Week 5: [Sep 20 Finish Go/No-Go Perceptron Results - Start SVMs] [Sep 22 SVMs]

Week 6 [Sep 27 Finish SVMs, Numpy] [Sep 29 Pillow, Neural Net Experiments, Feedforward Networks]

Week 7 [Oct 4 Estimators, Feedforward Networks] [Oct 6 MLE-Cost Functions, Cross-Entropy]

Week 8 [Oct 11 Practice Midterm] [Oct 13 Midterm]

Week 9 [Oct 18 Finish Cross-Entropy, Softmax Layers, Minimization Methods] [Oct 20 Stochastic Gradient Descent, Backpropogation]

Week 10 [Oct 25 Keras and Tensorflow] [Oct 27 Training and Evaluation using Keras - Regularization Data Augmentation]

Week 11 [Nov 1 Finish Regularization - Start Optimization] [Nov 3 Finish Optimization]

Week 12 [Nov 8 More Keras - CNNs and RNNs] [Nov 10 Recurrent Neural Networks]

Week 13 [Nov 15 Finish RNNs, Neural Network Tuning] [Nov 17 NN Design Methodology]

Week 14 [Nov 22 Finish NN Design, NN Applications] [Nov 24 - Thanksgiving]

Week 15 [Nov 22 More NN Applications] [Dec 1 Tensorboard, GANs]