AI for Decision Making

Recognizing Decision-Making Problems

Understand how to analyze and frame these real-world problems for AI solutions, setting the groundwork for effective decision-making strategies.

Preparing Data for Machine Learning

Learn how to define and curate datasets tailored for machine learning approaches to decision-making. This section covers the essentials of data collection, cleaning, and preparation, ensuring your machine-learning models are built on solid foundations.

Formulating Problems for Large Language Models (LLMs)

Explore how to define decision-making problems specifically for LLMs. This involves understanding LLMs' capabilities and structuring problems that leverage their general world knowledge and ability to communicate in natural language.

Mastering Prompting Techniques for Knowledge Extraction

Understand how to create effective prompts to extract knowledge and insights from LLMs. This topic provides practical skills in interacting with LLMs to support decision-making processes.

Evaluating Results of AI Models

After building an AI approach, the quality of the results needs to be evaluated. This topic covers estimating confidence in decisions using uncertainty and statistical significance metrics.

Addressing Limitations and Risks in AI Decision-Making

Identify the potential limitations and risks associated with using AI for decision-making. This focuses on developing strategies for risk mitigation, ethical considerations, and ensuring responsible use of AI technologies.