0 or 3 credits
Fall 2025 Lecture Laboratory Lower Division
This course provides an introduction to foundational areas of artificial intelligence and current techniques for building intelligent systems. As an entry-level course for Artificial Intelligence, the primary goals of this course are: Teach fundamental building blocks of an intelligent system, namely, knowledge representation; learning; model validation, diagnosis, and visualization; reasoning and decision-making; probabilities and uncertainty in AI. Provide students with first-hand experiences in building a working machine learning and an automated reasoning system. Provide an overview of current state-of-the-art technologies in multiple domains of artificial intelligence. Broaden the students' horizon and spark their interests via learning about exciting applications of artificial intelligence in many aspects of human society.
Learning Outcomes1Use data processing and implement learning and reasoning algorithms using Python.
2Determine how to represent knowledge in an AI system.
3Build a first-hand machine learning system. Know and be able to build all basic components of the entire machine learning pipeline, including data collection, wrangling, cleaning, model selection, training, cross-validation, and diagnosis.
4Master the mathematical theory behind linear regressors and classifiers.
5Build a first-hand automated reasoning system, learn how to formulate real-world problems as constraint programs, solve reasoning problems and make complex decisions using AI.
6Produce a reasoning system using depth-first search.
7Demonstrate basic knowledge of the full landscape of AI: game playing, causality, fairness/bias/ethics, uncertainty handling, reinforcement learning.
Prerequisites
Restrictions
made by Purdue students.