Course Syllabus

Meeting times: MWF 10am–6pm (lunch break 12–1pm) –– Jan. 4 – 23, 2017

Instructor: Thomas Serre (Assistant Professor, Cognitive Linguistic & Psychological Sciences)

Description:  This winter session course will provide an introduction to computing using the matlab programming language for students in the life sciences with no prior programming experience.

Objectives: At the end of this course, students will be able to implement matlab functions independently to solve many common programming challenges associated with the study of the mind, brain and behavior — from conducting statistical data analyses to handling basic input/output data for parsing and processing different file types to implementing psychophysics experiments.

Beyond learning to program, a life-long learning outcome is for students to develop computational thinking skills. That is, a way of solving problems that draws on fundamental concepts borrowed from computer science. Students from the life sciences often lack these skills because programming has not yet become an integral part of our education system. However, in our rapidly transforming society, computers have become such an integral part (from social media and big data to robotics and artificial intelligence) that computational thinking constitutes a much needed skill to flourish in modern society. Mastering these skills will enable students to more richly understand the cognitive, linguistic, and psychological sciences — and impact society.

Who should take this course: The course is designed for students in psychology, cognitive science, neuroscience and other non-computer science majors interested in learning matlab and, more generally, in developing computational thinking skills.

Prerequisites: None. This course is intended for students with no prior programming experience.

Course activities: There will be about 26 hours of preparatory reading work before class starts and students will have to take a short online quiz before the first day of class. The main course will be organized in 8 modules each blending lectures, whiteboard exercises, hands-on tutorials and labs (for a total of 64 hours total). The bulk of students’ work will consist in about 80 hours of work (8 mini-projects) that students will work on in between class meetings. The last day of class will consist in a 10-hour long hackathon where students will work in teams towards the completion of an independent project.

Breakdown of assessment:

• Prep work (1): 5%
• Labs (8): 30%
• Mini-projects (8): 40%
• Final project / hackathon (1): 25%

Grades: 90-100 A, 80–90 B, 70–80 C, <70 NC.