ECE 57000, Artificial Intelligence (Fall 2026)

Important Links

• Brightspace (official syllabus and course grades) — open the Syllabus tab for the current official syllabus.
• Tentative course schedule (PDF)
• Piazza (announcements and discussion)
• Gradescope (all submissions and grades)

AI Product documents

• AI Product Student Guide v1.0.0 (PDF)
• AI Product Milestone 1 Package v1.0.0 (ZIP) — Updated September 22 with format-checker fixes; required work is unchanged.
• Milestone 1 submissions are open on Gradescope. Immediate feedback checks formatting only; grading feedback will be released after grades are published.
• Fillable AI Product Stakeholder Agreement (PDF)
• Fillable Shared Stakeholder Agreement (PDF)
• Stakeholder Discovery and Design Thinking (PDF) — Last updated September 9, 2026.
  • Download Stanford’s Design Thinking Bootleg cards (PDF) (website). Start with Interview Preparation and Interview for Empathy (cards 3–4).
  • Design Council’s Double Diamond: process overview.

Assignments

All graded work will be submitted through Gradescope. Assignment links will be added as they are released.

Dynamic Course Materials

These materials form a chronological, organized course outline. They are the latest cleaned-up versions and may be revised as the course develops.

• Artificial Intelligence in the Age of AI (PDF). See the syllabus and schedule links above.

• Scale Your Intelligence (PDF).

• Dimensionality Reduction: Motivation and Problem Setup (PDF).

• Linear Transformations: What Do They Do? (PDF).

• Reconstruction and Principal Component Analysis (PDF).

• NumPy and Numerical Computation for PCA (PDF).

• Binary Prediction: Generalization, Evaluation, and the Limits of Locality (PDF).

• Learning and Interpreting Linear Regression (PDF).

• Logistic Classification: Scores, Probabilities, and Log Loss (PDF).

• Why the Shape of the Objective Matters (PDF).

Frozen Lecture Slides By Day

These date-specific copies remain fixed even when the topic decks above are later revised.

• Monday, August 24: Artificial Intelligence in the Age of AI (PDF).
• Wednesday, August 26: Course Launch and Working Effectively with AI (PDF).
• Friday, August 28: Course Launch Completion and Scale Your Intelligence (PDF).
• Monday, August 31: Scale Your Intelligence: Six Operating Principles (PDF).
• Wednesday, September 2: Mental Models and High-Dimensional Representation (PDF).
• Friday, September 4: Dimensionality Reduction and Linear Transformations (PDF).
• Wednesday, September 9: Stakeholder Discovery and Linear Transformations (PDF).
• Friday, September 11: Linear Transformations and Reconstruction: Choosing a Decoder Basis (PDF).
• Monday, September 14: Reconstruction and Principal Component Analysis: Formulation and Fixed-Total Decomposition (PDF).
• Wednesday, September 16: PCA: Optimality and Coordinate Variance (PDF).
• Friday, September 18: PCA Interpretation and NumPy Computation (PDF).
• Monday, September 21: Interpreting PCA and Formalizing Prediction (PDF).
• Wednesday, September 23: Binary Prediction: From the Goal to KNN (PDF).
• Friday, September 25: Why Fitting Examples Can Mislead Us (PDF).
• Monday, September 28: Choosing Models and Evaluating the Whole Pipeline (PDF).
• Wednesday, September 30: Learning and Interpreting Linear Regression (PDF).
• Friday, October 2: Linear Regression, Logistic Classification, and the Definition of Convexity (PDF).