Hector Munoz-Avila

Spring 2027: CSE 127 (COGS 127) Survey of Artificial Intelligence

Schedule: TR 9:20–10:35 (Main Campus)

Description:

Artificial Intelligence (AI) is rapidly transforming computing, engineering, science, and society. This course provides a broad and accessible introduction to AI, covering three major approaches: symbolic AI, machine learning, and generative AI. The emphasis is on understanding the fundamental ideas behind these approaches, their strengths and limitations, and how they relate to one another.

We will begin with foundational ideas about intelligence and intelligent agents and examine classical AI approaches, including search, planning, knowledge representation, rule-based reasoning, and knowledge graphs. We will then study the foundations of machine learning, including classification, gradient descent, and reinforcement learning.

A substantial part of the course will focus on modern Generative AI and large language models (LLMs). Topics will include prompting and reasoning with LLMs, embeddings and information retrieval, Retrieval-Augmented Generation (RAG), Graph RAG, RLHF, and agentic AI. We will examine not only what these systems can do, but also their limitations, including hallucinations, grounding, alignment, and ethical and societal concerns.

By the end of the course, students will be able to reason about how different AI approaches work and when each is appropriate, and understand how classical AI, machine learning, and Generative AI contribute to today's AI systems. The course is designed to be accessible to students from a variety of backgrounds and emphasizes conceptual understanding and intuition rather than advanced mathematics or programming.

Course work: There will be homework assignments, in-class exercises, and exams. The course emphasizes applying and explaining AI concepts rather than implementing large AI systems. There are no programming assignments in this course.

Prerequisites: CSE 004 or CSE 007 or CSE 012. Students who have taken other courses providing an elementary background in programming or computational thinking are encouraged to contact the instructor for permission to enroll.

Course ratings: based on students' evaluations of the Spring 2026 offering by the instructor.
• The course increased my knowledge of the subject matter: 4.78/5 (mean).
• Instructor was responsive to students' questions: 4.65/5 (mean).
• The instructor's teaching methods contributed to my understanding of the course material: 4.61/5 (mean).

---

Fall 2026: CSE 337/437 Reinforcement Learning

Schedule: TR –10:45-noon (Main Campus)

Description:

Reinforcement Learning (RL) is the framework behind systems that learn to make decisions through interaction, from robotics and game-playing agents to modern AI systems. This course introduces the foundations of RL as a framework for sequential decision-making under uncertainty.

Students will develop a strong understanding of the core principles that underlie modern RL systems, including challenges such as exploration, stability, and long-horizon credit assignment. The course also connects these foundations to contemporary AI, including deep reinforcement learning and the role of large language models (LLMs) and transformers in agentic and decision-making systems.

Through selected case studies, such as reinforcement learning from human feedback (RLHF) and LLM-based agents, we will examine how classical RL ideas are applied, extended, and sometimes break down in modern large-scale AI systems. By the end of the course, students will be equipped to reason about both the foundations of RL and its role in modern AI systems such as agentic AI. Agentic AI represents an evolution where LLMs provide the cognitive framework and reinforcement learning provides the optimization loop, allowing the system to act as a self-correcting autonomous agent.

Course work: There will be exams and homework assignments. There will be no final exam. Instead, students will complete a final project combining reinforcement learning and LLM-based agents, such as building a system that learns to make decisions over tools or strategies using feedback.

Course ratings: based on student's evaluation of the most recent offering by the instructor of this course.
• Instructor presented content in an organized manner: 4.5/5 (mean).
• The instructor's assignments (i.e. projects, homeworks, papers, etc.) provided opportunities for participative learning within the course: 4.75/5 (mean).
• The course increased my knowledge of the subject matter: 4.88/5 (mean)

---

Spring 2026: CSE 127 (COGS 127) Survey of Artificial Intelligence

Schedule: TR 9:20–10:35 (Main Campus)

Description:

• An introduction to artificial intelligence (AI) intended for non-majors
• Core AI concepts, systems, and a brief history of the field
• Machine learning fundamentals, including classifiers and reinforcement learning
• Generative AI, including large language models (LLMs) and agentic AI

---

Fall 2025: CSE 098-020 Leveraging Large Language Models (4Ls)

Schedule: TR 9:20–10:35 (Main Campus)

Description:

• Practical uses of modern Generative AI Chatbots
• Prompt engineering (task-oriented prompts, zero-shot vs. few-shot prompts, prompt chaining, prompt schemas, chain-of-thought prompts)
• Common pitfalls such as hallucinations, grounding challenges, and limitations in reasoning
• Ethical concerns including misinformation, bias, and privacy
• Students will gain a clear understanding of what they can and cannot expect from modern Generative AI Chatbots
• No prerequisites required.