MIDAS AI in Research

Part I — AI Across the Research Lifecycle

Chapter 1: Overview: Artificial Intelligence in Research
Chapter 2: How Modern AI Works
Chapter 3: Prompt Engineering
Chapter 4: When to Use AI in Research
Chapter 5: Literature Review with AI
Chapter 6: Research Planning and Hypothesis Development with AI
Chapter 7: AI-Assisted Writing and Research Communication
Chapter 8: Grant Writing with AI
Chapter 9: AI-Assisted Reviewing
Chapter 10: Ethics, Privacy, and Compliance
Chapter 11: Checking AI Output

Part II — AI in Data Analysis

Chapter 12: Getting Your Data: Access, Sources, and Compliance
Chapter 13: Computing Resources for AI Research
Chapter 14: Exploratory Data Analysis with AI
Chapter 15: Data Preparation with AI Assistance
Chapter 16: Feature Engineering for Research Data
Chapter 17: AutoGluon Fundamentals: Tabular Prediction
Chapter 18: Time Series Forecasting with AutoGluon
Chapter 19: Multimodal Learning with AutoGluon
Chapter 20: Pre-trained Models for Text, Vision, and Audio
Chapter 21: Validation and Interpretation of AI-Assisted Analysis
Chapter 22: Reproducibility in AI-Assisted Research

Part III — Building with Modern AI

Chapter 23: NLP with Pre-trained Language Models
Chapter 24: Building a Research Knowledge Base with RAG
Chapter 25: AI Agents: From Single Answers to Multi-Step Research Tasks
Chapter 26: LLM Evaluation and Fine-tuning

Part IV — Resources & Reference

Chapter 27: AI Resources at the University of Michigan
Chapter 28: External AI Resources for Research
Chapter 29: Quick Reference Templates
Chapter 30: AI Literacy Glossary
Chapter 31: Computing Fundamentals
MIDAS Video Resources

MIDAS AI in Research

Index

Index

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