ECE 57000: Artificial Intelligence (Fall 2022)

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 (8/22/2022) - Introduction to artificial intelligence
  2. Week 2 (8/29/2022) - PCA and linear algebra
  3. Week 3 (9/5/2022) - Introduction to machine learning
  4. Week 4 (9/12/2022) - Linear models and gradient descent
  5. Week 5 (9/19/2022) - Basics of deep learning
  6. Week 6 (9/26/2022) - CNNs and Clustering
    • Monday: Continue CNNs (see updated slides); CIFAR-10 demo with BatchNorm and residual networks (notebook, pdf)
    • Wednesday: Clustering; K-means clustering demo (notebook, pdf)
    • Friday: Spectral clustering (see clustering slides); Optional related reading: ML, Ch 25.4.3
  7. Week 7 (10/3/2022) - Review of probability