Core Concepts in AI

This course provides a comprehensive introduction to key concepts in artificial intelligence (AI) and machine learning (ML). Learners will explore essential vocabulary, the R.O.A.D. Framework, performance evaluation, and algorithm tradeoffs. Topics include data quality, inter-annotator agreement, and the strengths and weaknesses of AI methods. By the end, learners will be equipped with the foundational knowledge to navigate and assess AI and ML systems effectively.

This module provides an introduction to artificial intelligence (AI). It does not require any prior knowledge of AI and is suitable for briefing managerial, and non-technical leaders to improve knowledge, expectations, and communication for AI projects.

This module covers the statistical foundations of machine learning and the common metrics for evaluating machine learning and artificial intelligence performance.

This module introduces the most common algorithms used in AI and machine learning, including support vector machines, Naïve Bayes, decision trees, random forest, and neural networks. We will discuss the strengths and weaknesses of these algorithms for different classes of problems.

This module explores data types (nominal, ordinal, categorical) and the challenges of data labeling, including human cognitive limits and reference issues. A key focus is inter-annotator agreement—a method to measure labeling consistency, highlighting biases and inefficiencies in human and machine processes. Consistent labeling, often more impactful than advanced algorithms, is crucial for responsible AI.

This module introduces the most common resource considerations in AI, specifically memory, computational tradeoffs, query expressiveness, and algorithm performance.