ISC: Interdisciplinary Sciences Courses

Courses

ISC 5517   Buddhist Psychology

College of Health, Department of Psychology

2 sh (may not be repeated for credit)

Psychological overview of Buddhist theory and practice as they relate to everyday living, clinical practice and personal and transpersonal growth. Drawing from Theravada, Mahayana, Tantra and Zen, topics include four noble truths, suffering, concentration, jhanas, dependent origination, attachments, mindfulness, vipassana, nature of self, consciousness, compassion, insight, freedom, and enlightenment.

ISC 5517L   Buddhist Psychology Lab

College of Health, Department of Psychology

1 sh (may not be repeated for credit)

Students learn and practice different types of meditation to cultivate concentration and mindfulness during meditation and daily living. Construction of a personal mandala and regular class attendance and participation are required.

ISC 5905   Directed Study

College of Health, Department of Psychology

1-12 sh (may be repeated indefinitely for credit)

ISC 6529   Research Methods in Intelligent Systems and Robotics

College of Sci and Engineering, Department of Intelligent Systems & Robotics

3 sh (may not be repeated for credit)

A course in methods that are employed to conduct research in intelligent systems and robotics. The course addresses the conduct of literature reviews, identifying open-ended research questions and appropriate ways to answer research questions, particularly focusing on experimental design and data analysis. Students also learn how to summarize, interpret and report results. Issues pertaining to the conduct of research involving human participants are also addressed.

ISC 7248   Deep Reinforcement Learning

College of Sci and Engineering, Department of Intelligent Systems & Robotics

3 sh (may not be repeated for credit)
Prerequisite: EEE 6772

This course addresses deep learning and reinforcement learning and their combination in deep reinforcement learning. Topics include reinforcement learning techniques such as dynamic programming, value iteration, policy iteration, and actor-critic methods. Deep learning techniques include convolution neural networks and learning through backpropagation. These techniques will be combined to create learning policies for various control applications. Extensive software projects will utilize open source libraries from several sources. Students will implement solutions to various problems, including agents learning to play video games and bipedal walking robot simulations. Students are expected to have a background in data structures and algorithms, linear algebra, Calculus II or equivalent, linear differential equations, and control theory.

ISC 8980   Dissertation

College of Sci and Engineering, Department of Intelligent Systems & Robotics

1-24 sh (may be repeated for up to 24 sh of credit)

This course is the major individual research in a relevant research area. The dissertation reflects intensive research produced by the student and collaboratively developed with the student's graduate committee. Graded on a satisfactory/unsatisfactory basis only. Admission to candidacy, completion of all other doctoral program requirements, and permission are required.