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last updated: Sun, Dec 16, 2007, 2:00 am

Frequently Asked Questions
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Course Description

This course will cover modeling and data analysis for Cognitive scientists, scientists of other disciplines, and Engineers. Emphasis will be given to the Cognitive perspective, with examples drawn from topics relevant to cognitive scientists.

Topics may include programming tools, matlab, linear algebra, linear regression, nonlinear regression (polynomial and exponential fits), basic statistical analysis (mean, standard deviation, mode, median, variance, covariance, correlation, hypothesis testing), numerical solution of differential equations, optimization, curve fitting, and data visualization. Neural networks will be included as well if time permits.

The objective of this course is to give fundamental tools to the student which will allow the student to effectively analyze data from experiments, extract information, understand standard analyis tools commonly found in the literature of science and engineering, and communicate results effectively. The student will also be given many resources from which to draw in the future, thus allowing them to expand their knowledge and skills.

Theory lectured on in class will be followed up with readings to expand on the concepts, homeworks to give experience with the techniques, and additional references/readings of research work in the field applying these techniques (demonstrating how these techniques are applied in real life).




Weekly readings will be provided in pdf format on the handouts page. These will include book chapters and scientific papers which elaborate on (or demonstrate applications of) the concepts presented in class, section, and on assignments.


Grading will follow the fill-the-bucket principle. For each homework assignment and for the Midterm and Final test you will get score points. These will be added. The grade will be based on your score and the maximum achievable score. The course average will be scaled (only up if need be, not down!)


7 projects


1 midterm exam


1 final exam

total possible will be 1000 pts, plus bonus
100%+ bonus

 Cheating and Academic Honesty Policies

First of all please DON'T CHEAT!!! It detracts from your learning in this class. When you go into the world you won't have the skills you should have gained here. Our goal is to help you learn, so if you have any problems, please come speak with us and we will help you resolve them to the best of our ability. That being said, the definition of cheating must be defined clearly:

Cheating on exams involves any form of copying from another student, giving or getting answers from another student, acquiring information in any way from an external source during the exam, or giving information to or receiving information from another individual which you should not receive during an exam (ie theories, data, answers, etc). You may ask questions during an exam of the instructor or TA's at any time. The TA's are not to give answers directly, but may provide hints.

Cheating on homeworks involves duplicating another person's code. You are to write your own code, unless the instructional team provides a starter code, or sample code for you to use. You may not use code from sources other than this course. You may not copy another student's code. However, you ARE encouraged to help each other and discuss the homeworks and material from the course. It is often through explaining something that one learns that concept even better than before. But when it comes to writing the code, you must do the actual writing of your own code. Programming is very much something you must do as well as study to learn it well, very similar to driving.

The Standard academic honesty policies of the university apply during this course as well. Click here for details

Instructional Team


C. Alex Simpkins, MS, C. Phil.

email: csimpkin "at" ucsd "dot" edu

Office hours:

Mon 4-5
CSB115 (pending other class reservations)
Thurs 4-5
CRB 243A, (call to get in) 858-822-2421
Fri 3-5pm
Muir woods Coffee shop

UCSD Department of Mechanical and Aerospace Engineering, UCSD 0411

9500 Gilman Drive




Nick Butko

email: nbutko "at" cogsci "dot" ucsd "dot" edu

Office Hours:

Slavik Bryskin

email: vbryksin"at" ucsd "dot" edu

Office Hours:

Leo Trottier

email: leo"at" cogsci "dot" ucsd "dot" edu

Office Hours:

Labs and locations

For lab access: click here

TA Section
MWF 2:00 - 2:50pm
CSB 002
Lab A01
Fri 9-9:50am
CSB 115
Lab A02
Fri 10-10:50am
CSB 115
Lab A03
Wed 11-11:50am
CSB 115
Lab A04
Wed 12-12:50pm
CSB 115
Lab A05
Mon 1-1:50pm
CSB 115
Lab A06
Mon 12-12:50pm
CSB 115

Final Exam :12/12/2007 Wed 3:00p - 5:59p, location CSB 001