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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, python, 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).
Regular 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.
Yes! It is designed such that it can be taken remotely. Here's how to succeed doing it, and how we make it possible:
Succeeding in the class: So you can do it remotely (though you are encouraged to attend and interact directly). However, remote does not mean non-participatory. It is very important to ACTIVELY participate in the course, and get live feedback as much as possible. So please be sure to attend in some form all lectures, discussions, do the assignments, quizzes, project, any exercises we set up in lecture, ask questions, interact on piazza, email, come to office hours and interact during the project. If you passively attend the class, you will 1)not do as well in your grade, 2)not learn in the course. This has been well established through research over decades quite consistently. My goal is for each of you, regardless of where you start out in knowledge and experience, to walk away having learned something. There is always room to learn more so there should be something for everybody.
Grading will follow the fill-the-bucket principle. For each homework assignment, quiz, project, and for the 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!)
Tentatively:
(4) Assignments |
32% |
(4-5) Weekly Quizzes and class participation |
20% |
(1) Project |
32% |
(8) Discussion notebooks |
16% |
| Extra credit (SONA, 1 EC Quiz, EC surveys) | ~10% |
| total possible will be 1000 pts, plus bonus | 100%+ (~10%) bonus |
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 directly 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. If you locate code online to assist you in answering questions, do not simply copy and paste it, you must understand every line of code - this is critical for your learning of the material.
The Standard academic honesty policies of the university apply during this course as well. Click here for details
| Role | Instructor | office hours | |
| Instructor | C. Alex Simpkins, Ph.D. | rdprobotics "at"gmail"dot"com |
|
| Teaching Assistant | Sagarika Sardesai | ssardesai@ucsd.edu |
| TA/Instr. | Section | Type |
Time/day |
Location |
| All | All |
Lecture |
TBA |
TBA |
| Dr. Simpkins | A00 | 11am -1:50pm (LEC) | Tu/Th | SOLIS 109 |
| Sagarika | A01 | 2-2:50pm (LAB) | Tu/Th | SOLIS 109 |