ECE 47300, Intro. to Artificial Intelligence (Spring 2024)

Optional textbooks

The bracketed acronym is used for referencing these books.

  1. [DD] Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, 2023. https://d2l.ai/
  2. [DL] Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016. http://www.deeplearningbook.org
  3. [ML] Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, 2012. https://ebookcentral.proquest.com/lib/purdue/detail.action?docID=3339490
  4. [PY] Python Data Science Handbook by Jake VanderPlas, 2016. https://jakevdp.github.io/PythonDataScienceHandbook/

Lecture content by week

  1. Week 1 - Introduction to artificial intelligence
  2. Week 2 - PCA
    • Tuesday: (PCA continued), PCA demo (notebook, pdf )
    • Thursday: (PCA continued)
  3. Week 3 - Machine learning
  4. Week 4 - Linear models and gradient descent
  5. Week 5 - Loss functions and basics of deep learning
  6. Week 6 - Convolutional networks
  7. Week 7 - Natural Langauge Processing
  8. Week 8 - Natural Langauge Processing
    • Tuesday: (RNNs continued); Demo of character-level RNN generation (tutorial data, notebook, pdf); Demo of vanishing and exploding gradients (notebook, pdf);
    • Thursday: (RNNs continued)
  9. Week 9 - Natural Langauge Processing