Principles of Artificial Intelligence
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Course Descripton
DS 497. Principles of Artificial Intelligence. Specification, design, implementation, and selected applications of intelligent software agents and multi-agent systems. Computational models of intelligent behavior, including problem solving, knowledge representation, reasoning, planning, decision making, learning, perception, action, communication and interaction. Reactive, deliberative, rational, adaptive, learning and communicative agents and multiagent systems. Artificial intelligence programming.
Course Staff
The Spring 2019 offering of Principles of Artificial Intelligence is taught by Professor Vasant Honavar.
Course Schedule
Lectures: Tuesday, Thursday 9am - 10:20am, 208E Westgate Building
Office Hours:
Instructor: Vasant Honavar: Monday 4:00pm - 5:00pm,
Teaching Assistant: Junjie Liang: Thursday 4:00pm-5pm
Course Prerequisites
The prerequisites for the course include knowledge of programming, programming language concepts (functional programming, imperative programming, declarative programming, object-oriented programming, recursion, abstract data types), discrete mathematics (set theory, grapth theory, logic), calculus, basic probability theory and statistics, and data structures (lists, trees, graphs etc.) and algorithms (design and analysis).
In addition, students are expected to have the writing and presentation skills necessary for preparing written reports and presentations based on term projects.
If you are not sure whether you have the necessary background, please talk to the instructor.
Target Audience
This course is targeted to upper level undergraduate students in Data Sciences, Information Sciences and Technology and Computer Science at Pennsylvania State University who are interested in learning about Artificial Intelligence and its applications. The course should also be accessible to, and potentially of interest to graduate students from a variety of disciplines and programs including Electrical Engineeringi, Bioengineering, , Operations Research, Bioinformatics and Genomics, Neuroscience, Cognitive Psychology, Statistics, among others.
Course Objectives
The primary objective of this course is to provide an introduction to the basic principles and applications of Artificial Intelligence. Programming assignments are used to help clarify basic concepts. The emphasis of the course is on teaching the fundamentals, and not on providing a mastery of specific commercially available software tools or programming environments. In short, this is course is about the design and implementation of intelligent agents---software or hardware entities that perform useful tasks with some degree of autonomy. Upon successful completion of the course, students will have an understanding of the basic areas of artificial intelligence including problemsolving, knowledge representation, reasoning, decision making, planning, perception and action, and learning -- and their applications (e.g., big data analytics, data mining, computational discovery, information retrieval). Students will also be able to design and implement key components of intelligent agents of moderate complexity in a programming language of their choice (e.g., Java, Lisp, Prolog, R, Python) and evaluate their performance. Graduate students are expected to develop familiarity with current research problems, research methods, and the research literature in AI.
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