Course Description: This course provides a comprehensive introduction to Artificial Intelligence, its use cases and how it fits into the Data Science and Big Data ecosystem. We will cover the core concepts of computational modeling that make up intelligent agents and machines.
Prerequisites: DATA 602
References:
- The Secret Like of Programs: (SLP) Understand Computers – Craft Better Code, Jonathan E. Steinhard ISBN 9781593279707
- Artificial Intelligence: (AI) Foundations of Computational Agents, 2nd Edition David Poole and Alan Mackworth ISBN 9781107195394
Recommended Hardware: Windows, Linux Ubuntu or Mac laptop or desktop with Web and Internet access for Cloud based labs as assigned during lecture. Must have USBA port or adaptor if USBc only port
Course Format and Assignments: The students will complete three homework, several in class assignments, a midterm exam and two labs. This course incorporates hands-on labs and practical exercises to engage students and apply the topics that are learned.
Tentative Schedule
- Week-1: Introduction. What is AI? History of AI and Evolution of Machines. Class Survey
- Week-2: AI Hardware Fundamentals
- Week-3: AI Software Fundamentals
- Week-4: Knowledge Representation & Reasoning
- Week-5: Search, Pathfinding and Association
- Week-6: Rules based, workflows and Robotic Process Automation
- Week-7: Midterm Exam
- Week-8: Agent Architectures and Multi-Agent Systems
- Week-9: Learning to Act
- Week-10: Machine Learning and NLP
- Week-11: Computer Vision
- Week-12: AI in the Cloud, Edge & Embedded Computing
- Week-13: Ethics & Security
- Week-14: Robotics & Final Lab