ECE 47300, Intro. to Artificial Intelligence (Spring 2025)

Important Links

• Syllabus (pdf), tentative course schedule (pdf), and prerequisite quiz (ungraded)
• Brightspace (Grades)
• Piazza (Announcements and discussion)
• Gradescope (Quizzes and assignment submission) - You will need to click the link on Brightspace under “Content” and “Gradescope Link” the first time to link your account. After that you can use this link I believe.
• Google Colab (Online computing environment including GPUs)

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. [PPA] Patterns, predictions, and actions: A story about machine learning by Moritz Hardt and Benjamin Recht, 2022, https://mlstory.org/pdf/patterns.pdf
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/
5. [DL] Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016. http://www.deeplearningbook.org

Lecture content by week

1. Week 1 - Introduction to artificial intelligence
   • Tuesday: Introduction to AI. See syllabus, course schedule, and course project links above.
   • Thursday: Principal Components Analysis (PCA), Review of linear algebra (notebook, pdf), Broadcasting rules in NumPy (and PyTorch) (notebook, pdf); Related reading: DL, Ch.2