ECE 57000: Artificial Intelligence (Fall 2019)

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Important Links

Optional textbooks

The bracketed acronym is used for referencing these books in the schedule below.

  1. [DL] Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016. http://www.deeplearningbook.org
  2. [ML] Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, 2012. https://ebookcentral.proquest.com/lib/purdue/detail.action?docID=3339490
  3. [PY] Python Data Science Handbook by Jake VanderPlas, 2016. https://jakevdp.github.io/PythonDataScienceHandbook/

Course Schedule (Tenative)

L W Day Date Due by noon Topic Related readings
1 1 Mon Aug-19 Syllabus
2 Wed Aug-21 Introduction to A.I. [slides] Intro. to A.I., DARPA Overview of AI
3 Fri Aug-23 How to Select Papers / Overview of A.I. Topics [slides] How to Select Papers, [slides] Overview of A.I. Topics
4 2 Mon Aug-26 Intro. to ML [ML, Ch. 1], [slides] Intro. to Machine Learning
5 Wed Aug-28 [Quiz 1] (continued) HW1 Instructions, [slides] Intro. to Machine Learning (part 2)
6 Fri Aug-30 Clustering [notebook], [pdf of notebook]
3 Mon Sep-2 No class (Labor Day)
7 Wed Sep-4 HW1 Clustering (continued) [notebook], [pdf of notebook]
8 Fri Sep-6 Select 3 papers Clustering (continued) [notebook], [pdf of notebook]
9 4 Mon Sep-9 Brief Review of Linear Algebra [DL, Ch. 2], [notebook], [pdf of notebook]
10 Wed Sep-11 [Quiz 2] Brief Review of Linear Algebra (continued) HW2 Instructions, [notebook], [pdf of notebook]
11 Fri Sep-13 Brief Review of Linear Algebra (continued) [notebook], [pdf of notebook]
12 5 Mon Sep-16 Review of Probability [DL, Ch. 3], [ML, Ch. 2], [slides] Review of Probability (Part 1)
13 Wed Sep-18 Review of Probability (continued) [DL, Ch. 3], [ML, Ch. 2], [slides] Review of Probability (Part 2)
14 Fri Sep-20 HW2 Review of Probability (continued) [DL, Ch. 3], [ML, Ch. 2], [slides] Review of Probability (Part 3)
15 6 Mon Sep-23 [Quiz 3] Review of Probability (continued) [DL, Ch. 3], [ML, Ch. 2], [slides] Review of Probability (Part 4)
16 Wed Sep-25 Density Estimation [slides] Density Estimation (Part 1)
17 Fri Sep-27 Density Estimation (continued) [slides] Density Estimation (Part 2)
18 7 Mon Sep-30 Density Estimation (continued) [ML, Ch. 4], [slides] Density Estimation (Part 3)
19 Wed Oct-2 [Quiz 4] Gaussian Mixture Models [ML, Ch. 11], [PY 05.12], [slides] Gaussian Mixture Models (Part 1)
20 Fri Oct-4 Gaussian Mixture Models (continued) [slides] Gaussian Mixture Models (Part 2)
8 Mon Oct-7 No class (Oct Break)
21 Wed Oct-9 Optimization [DL, Ch. 4], [slides] Optimization, [notebook] NumPy Gradient Descent
22 Fri Oct-11 Optimization/PyTorch (continued) [notebook] PyTorch Gradient Descent
23 9 Mon Oct-14 Optimization/PyTorch (continued) [notebook] PyTorch Gradient Descent
24 Wed Oct-16 [Quiz 5] Optimization/PyTorch (continued) [notebook] PyTorch Gradient Descent
25 Fri Oct-18 Convolutional Networks HW3 Instructions, [notebook] Convolutionals
26 10 Mon Oct-21 Convolutional Networks (continued) Illustrations of various convolutions, Paper corresponding to illustrations
27 Wed Oct-23 Convolutional Networks (continued) [notebook] Convolutionals, [notebook] CIFAR-10 Tutorial, [pdf of notebook] Convolutionals, [pdf of notebook] CIFAR-10 Tutorial
28 Fri Oct-25 Generative Adversarial Networks (GAN) [slides] GANs
29 11 Mon Oct-28 HW3 Deep Convolutional GANs [slides] DCGAN, [notebook] DCGAN Tutorial (edited for MNIST), [pdf of notebook] DCGAN Tutorial (edited for MNIST), PyTorch Tutorial on DCGAN for faces, DCGAN Original Paper
30 Wed Oct-30 Invertible Normalizing Flows [slides] Normalizing Flows, [notebook] Change of Variables, [pdf of notebook] Change of Variables, GLOW paper
31 Fri Nov-1 [Quiz 6] Invertible Normalizing Flows (continued)
32 12 Mon Nov-4 Invertible Normalizing Flows (continued) Project Submission Instructions, [slides] Normalizing Flows
33 Wed Nov-6 Density Destructors [slides] Density Destructors
34 Fri Nov-8 Density Destructors (continued) [slides] Density Destructors, [code] Density Destructor Code
35 13 Mon Nov-11 [Quiz 7] Unsupervised Dimensionality Reduction/PCA [slides] PCA (and a few project notes)
36 Wed Nov-13 PCA [slides] PCA (and a few project notes), [notebook] PCA Demos
37 Fri Nov-15 Term paper/Code Autoencoders Project Submission Instructions, [slides] Autoencoders
14 Mon Nov-18 Presentations
Wed Nov-20 Presentations
Fri Nov-22 5-min video Presentations
15 Mon Nov-25 Presentations
Wed Nov-27 No class (Thanksgiving)
Fri Nov-29 No class (Thanksgiving)
16 Mon Dec-2 Presentations
Wed Dec-4 Presentations
Fri Dec-6 Reviews Presentations
Mon Dec-9 No exam
Wed Dec-11 No exam
Fri Dec-13 No exam