ECE 47300, Introduction to Artificial Intelligence (Spring 2026)

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)

Assignments

Please review general instructions and submit assignments on Gradescope.

1. Principles of Wise and Effective AI Use
2. Numerical Linear Algebra Algorithms in NumPy
3. Geometric Intelligence (SVD & PCA)
4. Generalization Error, Distribution Shifts & Adversarial Attacks
5. Logistic Regression
6. PyTorch Classifiers
7. Recurrent Neural Networks
8. Transformer from Scratch
9. Autoencoders

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. Introduction to artificial intelligence
   • Tuesday: Introduction to AI. See syllabus, course schedule, and course project links above.
   • Thursday: Wise and Effective Use of Large Language Models
2. Linear Algebra
   • Tuesday: Linear Algebra Introduction (notebook); Brief Review of Linear Algebra with Numpy (notebook)
   • Thursday: (Linear algebra continued); Related reading: DL, Ch.2
3. Principal Component Analysis (PCA)
   • Tuesday: Linear Dimensionality Reduction via PCA (notebook)
4. Machine Learning
   • Tuesday: Intro. to ML; Optional related reading: DL, Ch. 5.1; K-nearest neighbors (KNN) and evaluating ML methods, (pdf) (notebook); Optional related reading: KNN Classifier Notes, DL, Ch. 5.2-5.3
   • Thursday: (KNN continued)
5. Machine Learning
   • Tuesday: Guest Lecture by Prof. Timothy Rogers - A Brief Tour of GPUs
   • Thursday: Linear and Logistic Regression; Gradient Descent (notebook); Optional related reading: PY, Linear regression, DL, Ch. 5.1.4 (short), ML, Ch. 7 and Ch. 8 (in-depth)
6. Deep Learning
   • Tuesday: Loss functions and regularization ( notebook); Optional related reading: ML, Ch. 8 section 8.3; Basics of deep learning
   • Thursday: (Deep Learning Continued) PyTorch and Automatic Differentiation (notebook, html)
7. Convolutional Neural Networks and Computer Vision
   • Tuesday: Basics of convolutional neural networks (CNN) (notebook, html); Optional related reading: DL, Ch. 9;
   • Thursday: Midterm 1
8. Recurrent Neural Networks and Natural Language Processing
   • Tuesday: CIFAR-10 demo (notebook, html); BatchNorm and Residual Networks (notebook, html);
   • Thursday: Recurrent Neural Networks (RNN) (notebook); Character RNN Classification Demo (notebook); Character RNN Generation Demo (notebook)
9. Attention and Transformers
   • Tuesday: (RNNs continued); Attention and Transformers
   • Thursday: Demo of seq-2-seq language translation (notebook)
10. Spring Break
11. Review of Probability
    • Tuesday: Review of Probability; Optional related reading: DL, Ch. 3, ML, Ch. 2
    • Thursday: Density Estimation
12. Autoencoders
    • Tuesday: Autoencoders; Optional related reading: Introduction to VAEs by original authors (2019), Original VAE paper (2013), From Variational to Deterministic Autoencoders
    • Thursday: Midterm 2
13. Generative Models
    • Tuesday: (Autoencoders continued); Diffusion Models