This course will present advanced topics in Artificial Intelligence (AI), including inquiries into logic, artificial neural network and machine learning, and the Turing machine. Upon successful completion of this course, students will be able to: define the term 'intelligent agent,' list major problems in AI, and identify the major approaches to AI; translate problems into graphs and encode the procedures that search the solutions with the graph data structures; explain the differences between various types of logic and basic statistical tools used in AI; list the different types of learning algorithms and explain why they are different; list the most common methods of statistical learning and classification and explain the basic differences between them; describe the components of Turing machine; name the most important propositions in the philosophy of AI; list the major issues pertaining to the creation of machine consciousness; design a reasonable software agent with java code. (Computer Science 408)

Students play a game in which they place beans on numbers that represent the sum of two dice. Each time a number comes up in a dice roll, a corresponding bean may be removed. The first person who removes all his beans wins the game. Students mathematically analyze the game to develop strategies.

This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.

Includes problems related to reasoning with inference rules.

Discusses homework related to reasoning with inference rules; relations and interpretations.

Problems and solutions related to first-order logic, reasoning with equivalences, and reasoning with inference rules.

Problems and solutions related to first-order logic, reasoning with equivalences, and reasoning with inference rules.

Does excessive exposure to violent video games cause violent behavior? Does increased gun availability cause more crime or less? Causal claims permeate everyday life and are constantly the subject of "studies" reported in the newspaper. The material in Causal and Statistical Reasoning examines the nature of causal claims and the statistical sorts of evidence used to support them. The material is contained in: approximately 20 content modules, a repository of over 100 short case studies, and a "Causality Lab" that allows students to simulate the work a social scientist does in trying to discover what causes what from data. The material is meant to be used for three related purposes. One, it is meant for students who will only take one such overview of research methods course in service of consuming the newspaper intelligently and critically. Two, it is meant for students who will take a few statistics courses in order that they have an appropriate qualitative conceptual framework within which to learn statistical ideas, and three, it is meant for students interested in the foundations of quantitative causal models: called Bayes Networks. By adjusting the set of modules, cases, and causality lab exercises covered, professors and students can tailor the experience accordingly. The OLI project teaches annual summer workshops for faculty who are interested in learning how to integrate the material into their courses.

An introduction to human information processing and learning; topics include the nature of mental representation and processing; the architecture of memory; pattern recognition; attention; imagery and mental codes; concepts and prototypes; reasoning and problem solving.

This course will introduce you to cognitive psychology. Memory, along with attention, perception, language, and decision making, are among the most prominent topics within this broad and diverse field. Upon successful completion of this course, students will be able to: Identify underlying theoretical considerations in the field of cognitive psychology; Describe the historical context in which cognitive psychology emerged as a field; Define cognitive psychology as is was historically defined and is now defined; Identify the main academic fields and other subdisciplines of psychology to which cognitive psychology is tied; Describe the main findings in the primary areas of scientific research within cognitive psychology; Compare and contrast the theories associated within the primary areas of scientific research in cognitive psychology (e.g., models of memory, attention, etc.). (Psychology 206)

Algorithms and paradigms for developing embedded systems that are able to operate autonomously for years at a time within harsh and uncertain environments. Focus on systems that demonstrate high levels of deduction and adaptation. Draws upon a diverse set of computational methods from artificial intelligence, operations research, software engineering, and control. Topics include: real-time deduction and search, automated planning, scheduling and execution, model-based diagnosis and failure recovery, reactive planning, hybrid systems, and agent architectures.

Algorithms and paradigms for developing embedded systems that are able to operate autonomously for years at a time within harsh and uncertain environments. Focus on systems that demonstrate high levels of deduction and adaptation. Draws upon a diverse set of computational methods from artificial intelligence, operations research, software engineering, and control. Topics include: real-time deduction and search, automated planning, scheduling and execution, model-based diagnosis and failure recovery, reactive planning, hybrid systems, and agent architectures. Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.

What kind of coat will keep you the warmest, one made from cotton, steel wool or air? In this experiment, students test three materials to determine which one is the best insulator.

Supplementary work on individual or group basis. Registration subject to prior arrangement for subject matter and supervision by staff. Common Sense Reasoning for Interactive Applications This course will explore the state of the art in common sense knowledge, and class projects will design and build interfaces that can exploit this knowledge to make more usable and helpful interfaces. Course requirements will consist of critiques of class readings [about 2 papers/week], and a final project [paper or computer implementation project]. Grades will be based primarily on the projects, as well as a small component for class and online participation.

In Data Analysis: As Real World As It Gets, we feature resources for teaching about data and statistics as supported by the NCTM Standards (NCTM, 2000). Data collection and analysis can be an avenue into the meaningful mathematics and problem-solving skills needed by students in the twenty-first century. And an answer to the student question, Why do we have to study math? can be found when teaching mathematics with a real-world statistics approach.

How should you build a boat so that it carries the most cargo? In this experiment, students form teams to solve an engineering problem.

Students use a Although atoms contain both negatively and positively charged particles, they do so in equal amounts and carry no net charge. This balance can be temporarily disrupted by rubbing one material against another. One device, known as a Van de Graaff generator, uses a fast moving rubber belt to charge a metallic dome to nearly 200,000 Volts. This activity uses a Van de Graaff generator to study the behavior of electrical charges.

This in-depth, multi-part course takes you through evolutionary theory and mechanisms, from definitions to details, natural selection to genetic drift, mutations to punctuated equilibrium.

The following lesson will enable students to develop tactile and auditory patterns. As students observe, analyze, and make predictions about patterns they will enhance their problem-solving and reasoning skills.