ECE 57000: Artificial Intelligence (Fall 2020)

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 (8/24/2020) - Introduction to artificial intelligence
  2. Week 2 (8/31/2020) - PCA and linear algebra
    • Monday: Review of linear algebra (notebook, pdf); Related reading: DL, Ch.2
    • Wednesday: Continued from above. See updated PCA slides above.
    • Friday: See updated PCA notes above; PCA demo (notebook, pdf )
  3. Week 3 (9/7/2020) - Introduction to machine learning
  4. Week 4 (9/14/2020) - Linear models and gradient descent
  5. Week 5 (9/21/2020) - Basics of deep learning
  6. Week 6 (9/28/2020) - Clustering
  7. Week 7 (10/5/2020)
    • Monday: Spectral clustering continued; Review of probability; Optional related reading: DL, Ch. 3, ML, Ch. 2
    • Wednesday: Review of probability (continued); Optional related reading: see above.
    • Friday: Review of probability (continued); updated slides above
  8. Week 8 (10/12/2020) Density estimation and GMMs
  9. Week 9 (10/19/2020) Autoencoders, VAE and GANs
  10. Week 10 (10/26/2020) Generative Adversarial Networks (continued)
  11. Week 11 (11/2/2020) Normalizing Flows
    • Monday: No in-person/live lecture; Pre-recorded lecture will be posted Normalizing Flows; Change of variables demo (notebook, pdf)
    • Wednesday: No lecture because reading day
    • Friday: Normalizing flows (continued)
  12. Week 12 (11/9/2020) Iterative flows and language modeling
  13. Week 13 (11/16/2020) Project presentations
  14. Week 14 (11/23/2020) Word embeddings (Thanksgiving week)