Session 27: Final Presentations

Session 26: Final Presentation Rehearsal

Session 25: Getting Players to Play Your Game by Sean Baptiste (Fire Hose Games)

Session 24: Running a Game Studio (Guest Lecture by Michael Carriere and Jenna Hoffstein)

Session 23: Project 4 Feedback; Team Discussions

Session 22: Project 4 Check-in Presentations; Scope

Session 20: Writing in Games by Heather Albano and Laura Baldwin

Session 19: Working with Sound Designers by Richard Ludlow and Andy Forsberg (Hexany Audio)

Session 18: Fiction and Narrative in Video Games

Session 17: Working with Artists by Luigi Guatieri

Session 16: Team Dynamics

Session 15: Freedoms of Play by Scot Osterweil (MIT Game Lab); Project 4 Playtesting; Project 4 St r

Session 14: Aesthetics

Session 13: Project 4 Planning and Team Formation; Serious Games

Session 12: Project 3 Presentations; Project 4 Topics by Guest Pablo Suarez

Session 11: Game Development and Best Practices by Tim Cowan (EA Games)

Session 10: Project 3 Check-In; UI and Usability

Session 9: Guest Lecture – Hidetaka “SWERY” Suehiro (Access Games)

Session 8: Project 2 Presentations; Project 3; Digital Prototype with User Interference

Session 2: Project 1; Brainstorming and Low-Fidelity Prototyping

Session 7: Quality Assurance; Playtesting by Genevieve Conley (Riot Games); Focus Testing Results

Session 6: Agile Project Management

Session 5: Agile Software Development

Session 4: Project 2 Intro; Project 2 Elevator Pitches; Digital Prototypes; Project 2 Team Choice

Session 3: Revision Control; Game Engines; Vision Statement

Session 1: Introduction; Guest Pablo Suarez (Red Cross/Red Crescent Climate Center); Game Engines;

Foundation Models and Generative AI

Lecture 23: Model Merging, Cross-Modal Coupling, Course Summary

Lecture 22: Probabilistic Inference II

Lecture 21: Probabilistic Inference I

Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind

Lecture 17: Learning: Boosting

Lecture 16: Learning: Support Vector Machines

Lecture 15: Learning: Near Misses, Felicity Conditions

Lecture 14: Learning: Sparse Spaces, Phonology

Lecture 13: Learning: Genetic Algorithms

Lecture 12B: Deep Neural Nets

Lecture 12A: Neural Nets

Lecture 11: Learning: Identification Trees, Disorder

Lecture 10: Introduction to Learning, Nearest Neighbors

Lecture 9: Constraints: Visual Object Recognition

Lecture 8: Constraints: Search, Domain Reduction

Lecture 7: Constraints: Interpreting Line Drawings

Lecture 6: Search: Games, Minimax, and Alpha-Beta

Lecture 5: Search: Optimal, Branch and Bound, A*

Lecture 4: Search: Depth-First, Hill Climbing, Beam

Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems

Lecture 2: Reasoning: Goal Trees and Problem Solving

Lecture 1: Introduction and Scope

Lecture 25: Interpretability

Lecture 24: Robustness to Dataset Shift

Lecture 23: Fairness

Lecture 22: Regulation of Machine Learning / Artificial Intelligence in the US

Lecture 21: Automating Clinical Work Flows

Lecture 20: Precision Medicine

Lecture 19: Disease Progression Modeling and Subtyping, Part 2

Lecture 18: Disease Progression Modeling and Subtyping, Part 1

Lecture 17: Reinforcement Learning, Part 2

Lecture 16: Reinforcement Learning, Part 1

Lecture 15: Causal Inference, Part 2