Schedule | AI Agents for Engineers

Date	Lecture	Readings	Logistics
Tu 01/13	Lecture #1: Introduction to Large Language Models slides	Brown et al. (2020), Language Models are Few-Shot Learners (GPT-3) Bommasani et al. (2021), On the Opportunities and Risks of Foundation Models Kaplan et al. (2020), Scaling Laws for Neural Language Models OpenAI blog - Engineering Use Cases of GPT	HW 0 Released - Course Primer
Th 01/15	Lecture #2: Transformer Architecture Deep Dive slides	Vaswani et al. (2017), Attention Is All You Need Al-Rfou et al. (2019), Character-Level Language Modeling with Transformer Illustrated Transformer (by Jay Alammar)
Tu 01/20	Lecture #3: Pretraining and Finetuning Paradigms slides	Devlin et al. (2018), BERT - Pre-training of Deep Bidirectional Transformers Raffel et al. (2020), Exploring the Limits of Transfer Learning with T5 Zhang et al. (2023), Instruction Tuning LLMs for Generalization Meta blog - LLaMA 2 technical report
We 01/21			HW 0 Due
Th 01/22	Lecture #4: Tokenization, Embeddings, and Representations	Sennrich et al. (2016), Neural Machine Translation of Rare Words with Subword Units Mikolov et al. (2013), Word2Vec - Efficient Estimation of Word Representations Reimers & Gurevych (2019), Sentence-BERT - Sentence Embeddings using Siamese Networks OpenAI Cookbook - Understanding Tokenization	HW 1 Released - Tokenization and Finetuning
Tu 01/27	Lecture #5: Prompting 101: Zero-shot, Few-shot, and Chain-of-Thought	Kojima et al. (2022), Large Language Models are Zero-Shot Reasoners Wei et al. (2022), Chain-of-Thought Prompting Elicits Reasoning in LLMs OpenAI Cookbook - Best Practices for Prompt Engineering Zelikman et al. (2022), STaR - Self-Taught Reasoner
Th 01/29	Lecture #6: Agent Architectures (1): ReAct, LangChain, Autogen, MCP, CrewAI	Yao et al. (2022), ReAct - Synergizing Reasoning and Acting in Language Models Microsoft Research (2023), Autogen - Enabling Multi-Agent Conversations CrewAI Documentation LangChain Docs - Agent Executors and Planning
Fr 01/30			HW 1 Due
Su 02/01			HW 2 Released - Prompting and ReAct
Tu 02/03	Lecture #7: Agent Architectures (2): Autogen, CrewAI, MCP, LangChain	Microsoft Research (2023), Autogen - Enabling Multi-Agent Conversations CrewAI Documentation LangChain Docs - Agent Executors and Planning
Th 02/05	Lecture #8: Retrieval-Augmented Generation (RAG) and Human Feedback	Lewis et al. (2020), Retrieval-Augmented Generation for Knowledge-Intensive NLP Bai et al. (2022), Training a Helpful and Harmless Assistant with RLHF OpenAI Cookbook - RAG and Embeddings Anthropic blog - Fine-tuning vs RLHF vs SFT
Su 02/08			HW 2 Due \| HW 3 Released - Agentic Frameworks
Tu 02/10	Lecture #9: Tool Use and Function Calling with LLMs	(2023), Foundation Models for Decision Making (Stanford) (2024), A Survey of LLM Agents - Reasoning, Tool Use, and Planning (2024), A Survey on Tool-Augmented LLMs
Th 02/12	Lecture #10: Memory and State in LLM Agents
Su 02/15			HW 3 Due \| HW 4 Released - RAG and Memory
Tu 02/17	Lecture #11: Open Source LLMs and Finetuning
Th 02/19	Lecture #12: AMGPT for Additive Manufacturing	Elhaw et al. (2024), AMGPT: A large language model for contextual querying in additive manufacturing
Su 02/22			HW 4 Due \| HW 5 Released - MMR and AMGPT
Su 03/01			HW 5 Due
Tu 02/24	Lecture #13: Design Automation and CAD Integration	(2024), Query2CAD: Generating CAD models using natural language queries (2025), Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling
Th 02/26	Lecture #14: Manufacturing Process Control Agents	(2024), LLM-3D Print: Large Language Models To Monitor and Control 3D Printing
Tu 03/03	Spring Break - No Class
Th 03/05	Spring Break - No Class
Su 03/08			HW 6 Released - Agentic Manufacturing
Tu 03/10	Lecture #15: Vector Databases and LLMs	Jing, Z. (2024), When Large Language Models Meet Vector Databases: A Survey Douze, M., et al. (2024), The FAISS Library Taipalus, T. (2024), Vector database management systems: Fundamental concepts
Su 03/15			HW 6 Due \| HW 7 Released - Databases and Coding Assistants
Th 03/12	Lecture #16: Coding and Simulation Assistants
Su 03/22			HW 7 Due \| HW 8 Released - Agents for Scientific Discovery
Tu 03/17	Lecture #17: Agentic Alloy Discovery	Pak, P., Chandrasekhar, A., & Barati Farimani, A. (2025), Agentic Additive Manufacturing Alloy Discovery Ghafarollahi, A., & Buehler, M. J. (2024), AtomAgents: Alloy Design and Discovery through Physics-Aware Multi-Modal Multi-Agent Artificial Intelligence Ghafarollahi, A., & Buehler, M. J. (2024), Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems
Th 03/19	Lecture #18: Scientific Reasoning with LLMs	Yao, S., et al. (2023), Tree of Thoughts: Deliberate Problem Solving with Large Language Models Song, Z., et al. (2025), Evaluating Large Language Models in Scientific Discovery Huang, J. & Chang, K. C. C. (2023), Towards Reasoning in Large Language Models: A Survey Mondorf, P. & Plank, B. (2024), Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models (2025), Do Large Language Models Understand Scientific Argumentation?
Su 03/29			HW 8 Due \| HW 9 Released - VLM and Safety
Tu 03/24	Lecture #19: Agent Safety, Bias, and Guardrails	Inan et al. (2023), Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations Mazeika et al. (2024), HarmBench: A Standardized Unit Test for Benchmarking LLM Jailbreak and Defense Mechanisms Perez et al. (2022), Red Teaming Language Models with Language Models
Th 03/26	Lecture #20: VLM and Multimodal Agents	Liu et al. (2023), Visual Instruction Tuning (LLaVA) Yang et al. (2023), MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action Lin et al. (2023), VILA: On Pre-training for Visual Language Models
Su 04/05			HW 9 Due \| HW 10 Released - Agents for Robotics
Tu 03/31	Lecture #21: AI Agents for Robotics	Ahn et al. (2022), Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (SayCan) Driess et al. (2023), PaLM-E: An Embodied Multimodal Language Model Liang et al. (2022), Code as Policies: Language Model Programs for Embodied Control
Th 04/02	Lecture #22: AI Agents for Material Discovery	Bran et al. (2023), ChemCrow: Augmenting large-language models with chemistry tools Merchant et al. (2023), Scaling deep learning for materials discovery (GNoME) (2024), AutoScience: Multi-agent Collaboration for Scientific Discovery
Su 04/12			HW 10 Due \| HW 11 Released
Tu 04/07	Lecture #23: AI Agents for CFD and FEA	Li et al. (2023), Physics-Informed Machine Learning for Fluid Dynamics: A Survey (2023), A Review of Large Language Models for Engineering Design (2024), Physics-Informed Agentic RAG
Th 04/09	Carnival - No Class
Su 04/19			HW 11 Due
Tu 04/14	Lecture #24: Massive Document Processing	Lee et al. (2023), REPLUG: Retrieval-Augmented Black-Box Language Models (2024), Unstructured Data Processing with LLMs Chen et al. (2024), LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Th 04/16	Lecture #25: AI Agent Workshop
Tu 04/21	Lecture #26: Project Presentations #1
Th 04/23	Lecture #27: Project Presentations #2