last updated:
The schedule and syllabus are subject to change. Updates will be posted to this Website and canvas, and additional content to the github repositories linked from this site. Any relevant changes will be announced in class or via email, but it is each student's responsibility to frequently check the online syllabus/schedule during the quarter. The class piazza can be accessed here (link will be provided once we switch to self-add mode, for now it should sync to the roster).
From the course catalog: Project-based course in which students will use computational notebooks to perform exploratory data analyses and to test hypotheses in large neuroscience datasets, including the differences between unique neuron types, leveraging text mining of the neuroscience literature, and human neuroimaging analyses.
Regular readings will be provided in pdf format on the assignments page (may be password protected, password given on canvas home/syllabus summary link) and on canvas. Generally we will duplicate the canvas and the website. These may include book chapters and scientific papers which elaborate on (or demonstrate applications of) the concepts presented in class, section, and on assignments. There will be approximately 4 assigned readings with an associated canvas quiz asking questions about the reading.
Grading will follow the fill-the-bucket principle. For each homework assignment, quizzes, and for the final projectyou 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!). There will be class participation credit exercises offered that you can complete on canvas (surveys, SONA credit, other possible ones, all can be completed remotely). There will be regular quizzes on canvas. They will consist of automatically graded questions (mult. choice mostly) and possibly some short answer that provide credit for a reasonable response. Anything written by you will be checked against chatbot/AI detectors, and anything that is predicted as having any likelihood of being written artificially will not receive credit. Plus if you do that you will miss out on learning!
Tentatively:
| Additional exercises, EC assignments, timing bonuses, SONA (2% for 2h), etc | ~>=10% |
Participation (class exercises, quizzes, activities, collected on canvas or google forms) |
20% |
5 assignments (individual, jupyter notebook) |
40% |
4 Readings with canvas quizzes for each |
20% |
1 Final project (group), NO FINAL EXAM |
20% |
| total possible will be 1000 pts, plus bonus | 100%+ bonus |
| Behavioral factor multiplier, boolean and defaults to 1, explained in lecture | (30% effect) |
Course grades will be assigned according to the following scale: ≥ 97%: A+; ≥ 94%: A; ≥ 90%: A-; ≥ 87%: B+ etc.
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 (open note and open book exams and quizzes allow for external sources - for example to open the lecture slides etc), 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 and IA's at any time. The TA's and IA's are not to give answers directly, but may provide hints.
Cheating on homeworks involves duplicating another person's code, papers, etc. You are to write your own papers, unless the instructional team provides some starter document, or sample document for you to use, or for group projects. You may not use documents from sources other than this course except for reference/citation, or where specified in the assignment. You may not copy another student's documents (group assignments are handed in together of course). 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/paper/etc, you must do the actual writing of your own and understand it. Research 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
C. Alex Simpkins, Ph.D.
email: rdprobotics "at"gmail "dot" com
csimpkinsjr "at" ucsd "dot" edu
Time/day |
location |
| Sat 2-3pm | Zoom, link provided weekly |
| Tu 3:20-3:50 after lecture | SOLIS (outside classroom) |
| Th 3:20-3:50 after lecture | SOLIS (outside classroom) |
| by appointment | by appointment, zoom is easiest |
UCSD Department of Cognitive Science, UCSD 0515
9500 Gilman Drive
92093-0515
TA:
Siddhant Salvi
email: ssalvi 'at' ucsd 'dot' edu
Office Hours:
| TA | Section | Type |
Time/day |
Location |
| All | All |
Lecture |
TuTh 2-3:20 |
SOLIS107 |
| Siddhant | A01 |
Discussion A01 |
Wed 11-11:50am |
WLH2208 |