ECE 57000, Artificial Intelligence (Fall 2025)

Important Links

• Syllabus (pdf), tentative course schedule (pdf), and prerequisite quiz (ungraded)
• Piazza (Announcements and discussion)
• Gradescope
• Brightspace (Grades only)
• Google Colab (Online computing environment including GPUs)

Course Project

• AI Course Project Track Descriptions
• Project Checkpoint 1: Preliminary Code and Results

Assignments

Please review general instructions and submit assignments on Gradescope.

• (Optional bonus assignment) AI in the News

1. Project Primer
2. Linear Algebra, Numpy, and PCA Practice Questions
3. PCA and Power Iteration Algorithms
4. Building Classifiers (KNN, Logistic Regression, Model Evaluation); Notebook template: Notebook Template

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
   • Monday: Introduction to AI. See syllabus, course schedule, and course project links above.
   • Wednesday: (Continued)
   • Friday: (Continued)
2. Week 2 - PCA and Linear Algebra
   • Monday: Labor Day
   • Wednesday: Wise and Effective Use of Large Language Models.
   • Friday: Linear Algebra and Unsupervised Dimensionality Reduction via PCA. Review of linear algebra (notebook, pdf), Broadcasting rules in NumPy (and PyTorch) (notebook, pdf); Related reading: DL, Ch.2
3. Week 3 - PCA and Linear Algebra
   • Monday: (Continued).
   • Wednesday: (Continued).
   • Friday: Unsupervised Dimensionality Reduction via PCA (Part 2); PCA demo (notebook, pdf)
4. Week 4 - Machine Learning
   • Monday: Intro. to ML; Optional related reading: DL, Ch. 5.1
   • Wednesday: K-nearest neighbors (KNN) and evaluating ML methods, (pdf); KNN Demo (notebook, pdf); Optional related reading: KNN Classifier Notes, DL, Ch. 5.2-5.3
   • Friday: (KNN continued)
5. Week 5 - Linear Models and Gradient Descent
   • Monday: Linear and Logistic Regression; Optional related reading: PY, Linear regression, DL, Ch. 5.1.4 (short), ML, Ch. 7 and Ch. 8 (in-depth)
   • Wednesday: Gradient Descent (notebook)
   • Friday: Loss functions and regularization; Optional related reading: ML, Ch. 8 section 8.3
6. Week 6 - Deep Learning
   • Monday: Basics of deep learning
   • Wednesday: (Continued) PyTorch and Automatic Differentiation (notebook, html)
   • Friday: Basics of convolutional neural networks (CNN) (notebook, html); Optional related reading: DL, Ch. 9
7. Week 6 - Convolutional Networks
   • Monday: (Continued); CIFAR-10 demo (notebook, html);
   • Tuesday: BatchNorm and Residual Networks (notebook, html)