ESP Biography

We will cover basic principles of game AI before designing an AI as a class! The discussion will not cover mathematical/implementation details, so don't worry if you feel that your math is a bit shaky or if you don't have programming experience yet!

We will briefly cover the history of AI before discussing a variety of algorithms that are representative of major paradigms. The objective of our session will not be to understand the details of particular algorithms, but rather to get a feel for how one can approach various problems in AI.

Curious about how we can make machines more intelligent? Come learn the basic principles of modern AI! We will introduce the problem formulations of search, regression, classification, and reinforcement learning.

We will survey perspectives and techniques in modern Artificial Intelligence. In particular we will discuss the assumptions and objectives of the classification and reinforcement learning paradigms.

We we will begin with a discussion of what constitutes AI and its capabilities and limitations. Then we will learn about a paradigm of AI called reinforcement learning before diving into a classic reinforcement learning algorithm. Finally we will take a problem and fit it to work with the algorithm.

This course will provide a broad overview and understanding of the underpinnings of established artificial intelligence techniques. In addition, we will develop a simple AI algorithm for one or two specific problems as a class if time permits.

Perspectives and techniques in artificial intelligence. Depending on interest, we will cover basic algorithms in one or more of the following areas: motion planning, natural language processing, vision, classification.

Basic methods and approaches in AI and their applications. Specific topics that we will discuss will be determined by in-class interest. Topics can include the following: vision, motion planning, classifiers (Naive Bayes or Decision Trees), search space formulation.

Exposure to the modern paradigms of AI and some of the problems that AI researchers are tackling. One simple but powerful model will be discussed (Bayes nets).

Learn how AI works at a high level and it's limitations. If interest permits, specific algorithms will be discussed.

Overview of the field of AI. Some basic algorithms and concepts such as searching, vision, and logical deduction.

Topics include an overview of Robert Lang's circle-river packing (tree method), tessellation, and point splits. Origami diagramming notation and basic folds will be explained for those with no exposure.