📚 gabe's wiki
- Discussion 1
- Lecture 1.2: Project Tips
- Lecture 1.3: Prerequisites
- Lecture 1.4: AI Traditions & History
- Lecture 2.1: Search Intro
- Lecture 2.2: Best-First Search
- Lecture 2.3: Informed Search
- Lecture 2.4: Adversarial Search
- Lecture 4.1: Machine Learning with Decision Trees
- Lecture 4.2: Machine Learning with Decision Trees Continued
- Lecture 7.1: Reinforcement Learning Intro
- Lecture 7.2 Reinforcement Learning Terminology
- Lecture 7.2: RL Terminology
- Lecture 7.4: Policy Gradient Methods
- Module 1
- Module 2
- Adding Substance to Machine Learning Projects
- FAQs (All Classes)
- Microphone Placement
- PTA Numbers
- Teaching Schedule
- Using GitHub Classroom
- AI in the Classroom
- Automating Forge Repository Setup in Emacs
- Can I present in your class?
- Emacs as a Lecture Tool
- Getting Comfortable
- Goodhart's Law and Assumptions about Incentives
- Installing spaCy models with uv
- Interrupts
- NAAPS Model: The Super Short Version
- Programmer's Distaste
- Remapping Discord Keybindings
- Second-Order Consequences
- Storage on the Workstation
- The Agency-Consciousness Matrix
- Thinking with Labels, Thinking with Causes
- Three Uses of the word "Model" in Machine Learning
- Time Management Questions
- True Workflow Improvement and Workflow Improvement Anti-Patterns
- QR Code
- Recommendation Letters
Easy Debugging Tricks