Syllabus

BCSC 512: Syllabus

Spring 2022

Instructor: Robert Jacobs
Meliora 306
585-275-0753

This course focuses on:

  • The mathematical foundations of deep neural networks (DNNs).
  • Knowledge of how to implement DNNs using the Python programming language and the Keras library.
  • The uses of DNNs in the cognitive science and neuroscience literatures.

Prerequisites:

  • Knowledge of calculus. Knowledge of linear algebra and probability theory will also be helpful (though prior knowledge of these areas is not strictly required).
  • Knowledge of computer programming. Homeworks require students to write programs implementing DNNs using Python (including scipy, numpy, and matplotlib) and the Keras library. Everyone must write their own code. If you do not know how to program with Python (again, including scipy, numpy, and matplotlib), then your first homework assignment is to complete a tutorial.

Readings

  • Mathematical notes written by the instructor. Please bring these notes to class.
  • Journal articles and book chapters made available throughout the semester.

Requirements

  • Class attendance and participation are mandatory.
  • Readings are mandatory.
  • There will be three homework assignments. Assignments include mathematical problems to solve and/or computer models to implement. You will have two weeks to complete the first assignment, one week to complete the second assignment, and one week to complete the third assignment.
  • Students will give formal presentations of readings. Each presentation should be limited to 20-25 minutes. Students should use PowerPoint slides during their presentations.
    Critically, assume that audience members have NOT read the reading. Tell the audience:
    • What is the primary research question or issue studied in the reading?
    • What do the authors do to address this research question or issue?
    • What are the authors' main conclusions?
    • Think of two (or more) questions (or comments or discussion points) regarding the reading.
  • Final project, reported in a paper and class presentation: Each student will conduct a final project on a topic of his/her choosing (subject to approval by the instructor). The paper describing the project should have a maximum length of 3000 words. The class presentation should have a length of 20-25 minutes. Students may work alone but are strongly encouraged to collaborate with another stu- dent. A collaborating pair should write a single paper (maximum length: 6000 words) and give a single presentation (e.g., 40-50 minutes).

Course Grading

  • Class participation and presentation of papers: 30%
  • Homework assignments: 35% (HW 1: 15%; HW 2: 10%; HW 3: 10%)
  • Final project (paper and class presentation): 35%

Collaboration Policy and Academic Honesty

Unless a collaboration is specifically authorized by the instructor, each assignment must be completed by each individual student working alone. Any student suspected of cheating will be referred to the Board on Academic Honesty for investigation and possible penalties. Any evidence of duplication or plagarism (e.g., copying someone else's writing, or failing to cite the work, ideas, or writings of someone else, and presenting it as your own) will be referred to the Board on Academic Honesty. For more information, see the Honesty website.

Learning Assistance

Students requiring assistance in learning should contact the Center for Excellence in Teaching and Learning (CETL) at Dewey 1-154 (phone: 585-275-9049; ; website).

Disability Resources

This course respects and welcomes students of all backgrounds and abilities, and we encourage students to talk with us about any concern or situation that affects their ability to complete their academic work successfully. Students requiring accommodations should contact the Offce of Disability Resources in Taylor Hall (phone: 585-276-5075; ; website).

College Course Credit Hour Policy

This course follows the College credit hour policy for three-credit courses. This course meets two times weekly for three academic hours per week. The course also includes independent out-of- class assignments. In this course, the independent out-of-class assignments include readings of large and/or difficult academic papers and an independent project reported in a final paper and presentation.