IB 496

Spring 2019 All Classes

All Classes

Special Courses

SHOW FINAL EXAM SCHEDULE

Credit: 1 TO 5 hours.

Experimental and temporary courses.

Additional fees may apply. See Class Schedule. 1 to 5 undergraduate hours. 1 to 4 graduate hours. Approved for letter and S/U grading. May be repeated as topics vary. Prerequisite: Consent of instructor.

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IB 496 class schedule data for spring 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
65854	Lecture	AW3	3:00PM -3:50PM	MWF	Everitt Laboratory	Alleyne, M Wissa, A	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 3 hours Section Title: Bio-Inspired Design Section Info: ME 498/IB496 offers a unique interdisciplinary advanced design experience in the field of bioinspiration. During the course we will cover four focus areas: locomotion, sensing, materials, and complex systems. For each topic, we will discuss the state of the art on engineering side and the solutions in nature that can augment the current engineering systems. By the end of the course you should be able to work in interdisciplinary teams, use analogical design concepts, and produce a prototype based on a biological function to solve an engineering challenge in one of the four focus areas. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
65897	Lecture	AW4	3:00PM -3:50PM	MWF	Everitt Laboratory	Alleyne, M Wissa, A	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 4 hours Section Title: Bio-inspired Design Restriction(s): Restricted to Graduate - Urbana-Champaign.
68712	Lecture-Discussion	D	2:00PM -3:20PM	TR	Siebel Center for Comp Sci	Fuller, B	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 3 hours Section Title: Analysis of Biological Data in Section Info: 3 hour. Topic: Analysis of Biological Data in R This course will focus on developing skills in the use of R. It will cover the book 'The Analysis of Biological Data' by Whitlock and Schluter who created a website with R code to accompany the book. It will also use 'The R Graphics Cookbook". Students will be assigned weekly problem sets to be performed in R and will share their R-code and show how they solved problems. Weekly quizzes will occur on the assigned reading. There will also be student presentations of data analyses. Students must bring their own laptops to class. No prior experience with R is necessary.