- Course: CS 395 T (51903) Neural Computation (aka Neuroscience for Computer Scientists)
- Semester: Fall 2017
- Location: GDC 4.516
- Time: TTH 12:30PM - 2:00PM
- Alexander Huth
- office hours: Mon 9:30AM - 11:00AM, Wed 9:30AM - 11:00AM
- office: NHB 3.134
- email: [email protected]
Inferring what algorithms are used by existing computational systems. Using black box system identification to understand the function of real neural/brain systems. Using gradient propagation and other methods to understand the function of artificial neural networks.
By the end of this course, you should come away with an understanding of (1) the basic strategies used for inferring function from computational systems; (2) specific tools and techniques for studying the function of real and simulated neural systems; and (3) when to trust real data acquired from noisy systems.
The majority of the course will consist of lectures by the professor. For several classes, students will be asked to read and present papers to the rest of the students. Students will also be asked to give a slide presentation on the results of their final project during the last few classes of the semester.
Read the course materials. Ask questions if any topics are unclear. Be respectful of each other and the instructor. Have fun! :)
Students should have some previous programming experience (preferably in python). Discuss your situation with the instructor if you think you might not fulfill these prerequisites.
This page serves as the syllabus for this course. This syllabus is subject to change; students who miss class are responsible for learning about any changes to the syllabus.
There is no required course text book. Readings will primarily come from papers and online sources, including tutorials.
Additional required readings will be made available for download from the schedule page of the course website.
There will be no midterm or final exam.
There will be 2 homework assignments and a course project. Assignments will be posted as the semester progresses. A tentative [[schedule]] for the entire semester is posted on the schedule page. Readings and exercises may change up to one week in advance of their due dates.
There are several components to the class grade.
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Project (40%): There will be a group project (maximum 2 students) that will involve investigating the function of either a real computational system (using publicly available neuroscience data) or a neural network. There are three stages to this project: proposal, presentation, and final write-up.
- Proposal (5%): Before embarking on the project, each group will submit a proposal describing the topic and goal of their project.
- Presentation (20%): Each group will give a slide presentation describing the goals and results of their project.
- Write-up (15%): Each group will submit a write-up of their project.
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Problem sets (40%): There will be two problem set assignments. Each assignment is worth 20% of your course grade.
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Class participation (20%): Showing up for class, demonstrating preparedness (i.e., doing the readings), and contributing to class discussions. Attendance is required since there will be many in-class discussions.
Grading scale for problem sets: problem sets will be returned with feedback less than 2 weeks after the due date.
The presentation and final project write-up will be graded based on rubrics that will be made available to you before the due dates.
Homework is due in class on the noted due date. Homework must be turned in on the due date in order to receive full credit. Homework turned in less than 1 week late will be accepted but the score will be penalized by 10%. Homework later than 1 week will not be accepted.
Late homework will also be accepted under exceptional circumstances (e.g., medical or family emergency) and at the discretion of the instructor (e.g. exceptional denotes a rare event) with no penalty. This policy allowing for exceptional circumstances is not a right, but a privilege and courtesy to be used when needed and not abused. Should you encounter such circumstances, simply email assignment to instructor and note "late submission due to exceptional circumstances". You do not need to provide any further justification or personally revealing information regarding the details.
You are encouraged to discuss problem sets with classmates, but all written submissions must reflect your own, original work. If you worked with other students on a problem set, please include their names in a statement like "I worked on this course with XX and YY" on the assignment. If in doubt, ask the instructor. Acts like plagiarism represent a serious violation of UT's Honor Code and standards of conduct:
http://deanofstudents.utexas.edu/sjs/scholdis_plagiarism.php
http://deanofstudents.utexas.edu/sjs/conduct.php
Students who violate University rules on academic dishonesty are subject to severe disciplinary penalties, such as automatically failing the course and potentially being dismissed from the University. Don't risk it. Honor code violations ultimately harm yourself as well as other students, and the integrity of the University, policies on academic honesty will be strictly enforced.
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