04-655   Artificial Intelligence for Engineers

Course description

This is a graduate-level course on Artificial Intelligence designed to present a larger view of AI covering principles and techniques. It is primarily intended for those with backgrounds in engineering, science and IT who have not previously taken the subject formally or have been exposed to only some of the constituent elements. The course provides the foundations for further study, research, and application in different areas.

Topics include: Intelligent agents, search, games, CSP, knowledge representation, logic, reasoning, planning, probabilistic reasoning, decision making, Machine Learning basics, Neural Networks, deep learning, Natural Language Processing basics, machine vision, AI in robotics, and ethics and philosophy of AI.

Several assignments and a final project will be given to enable the student to gain practical experience in applying these techniques.

Learning objectives

Successful students should be able to understand what constitutes "Artificial" Intelligence, identify systems with Artificial Intelligence, and explain how AI enables capabilities that are beyond conventional technology.

Outcomes

Upon course completion, students should be able to:

• Apply classical AI techniques including search algorithms, minimax algorithms, neural networks, tracking, and robot localization
• Apply AI techniques for problem-solving
• Explain the limitations of current AI techniques

Content details

• Intelligent agents
• Search
• Games, constraint programming
• Knowledge, logic
• Probabilistic reasoning, planning, decision making
• Machine learning basics
• Neural network and deep learning basics
• Natural language processing basics
• Advanced topics and ethics of AI

Prerequisites

None. However, an ability to code in a programming language and an understanding of probability are expected. Prospective students should consult the instructor if they are either lacking or have already taken another AI course.

Faculty

Ahmed Biyabani