WEEK 5: GUEST LECTURE - GENERATIVE AI FOR PROGRAMMING

Large Language Models, Autonomous Agents, and AI-Assisted Programming

October 13, 2025

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Weekly Materials

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Guest Lecture: Generative AI for Programming

Professor's lecture slides (PDF)
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Code Examples

GitHub Copilot Claude Code
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References & Resources

Understanding Large Language Models Stephen Wolfram's explanation of how LLMs work
OpenAI GPT-4 Technical Report Technical details about GPT-4 architecture

Additional Notes

Week 5: Guest Lecture - Generative AI for Programming

Guest Lecturer

Anna Smirnova - PhD Candidate

Learning Objectives

  • Understand the landscape of AI tools for programming (chatbots, assistants, agents)
  • Distinguish between standard and reasoning AI models
  • Learn effective prompt engineering techniques for code generation
  • Understand the capabilities and limitations of AI-assisted programming
  • Get hands-on experience with GitHub Copilot and other AI coding tools

Topics Covered

  • The AI Landscape: Three types of AI tools for coding
    • Chatbots (ChatGPT, Claude)
    • Code Assistants (GitHub Copilot, Cursor)
    • Autonomous Agents (Devin, Claude Code)
  • Understanding AI Models: Standard vs. reasoning models
  • Practical Usage: How to effectively use GitHub Copilot
  • Prompt Engineering: Techniques for better AI assistance
  • Reality Check: Benefits, limitations, and ethical considerations
  • The Future: Autonomous coding agents and their implications

Schedule

  • Guest Lecture: Monday, October 13, 2025 (10:15 - 12:00)
  • Live Demo: Using AI tools for programming tasks
  • Q&A Session: Discussion about AI’s role in data science

Key Concepts

  • Large Language Models (LLMs) architecture
  • Transformer models and attention mechanisms
  • Context windows and token limits
  • Fine-tuning vs. prompt engineering
  • Chain-of-thought reasoning
  • AI safety and alignment

Practical Skills

  • Using GitHub Copilot effectively
  • Writing effective prompts for code generation
  • Debugging AI-generated code
  • Understanding when to use (and not use) AI assistance
  • Evaluating AI tool outputs critically

Important Considerations

  • Academic Integrity: Proper attribution and understanding
  • Code Quality: AI as a tool, not a replacement for learning
  • Security: Risks of copying AI-generated code without review
  • Bias: Understanding limitations and biases in AI systems

Additional Resources

  • GitHub Copilot documentation
  • OpenAI and Anthropic research papers
  • AI safety and ethics guidelines