ECE 57000: Artificial Intelligence (Fall 2021)

Assignments / Project Checkpoints

Optional textbooks

The bracketed acronym is used for referencing these books.

  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/

Lecture content by week

  1. Week 1 (8/23/2021) - Introduction to artificial intelligence
  2. Week 2 (8/30/2021) - PCA and linear algebra
  3. Week 3 (9/6/2021) - Introduction to machine learning
  4. Week 4 (9/13/2021) - Linear models and gradient descent
  5. Week 5 (9/20/2021) - Basics of deep learning
  6. Week 6 (9/27/2021) - CNNs and Clustering
    • Monday: Continue CNNs (see updated slides); CIFAR-10 demo with BatchNorm and residual networks (notebook, pdf)
    • Wednesday: Finish CNNs, BatchNorm and residual networks; Clustering;
    • Friday: K-means clustering demo (notebook, pdf)
  7. Week 7 (10/4/2021) - Review of probability
  8. Week 8 (10/11/2021) - Density estimation and GMMs