Summary

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Expected Learning

Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course you’ll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Syllabus

Part I: Fundamentals of AI

• Overview of AI
• Statistics, Uncertainty, and Bayes networks
• Machine Learning
• Logic and Planning
• Markov Decision Processes and Reinforcement Learning
• Hidden Markov Models and Filters
• Adversarial and Advanced Planning

Part II: Applications of AI

• Image Processing and Computer Vision
• Robotics and robot motion planning
• Natural Language Processing and Information Retrieval

Required Knowledge

Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.