BCSC 229: Syllabus

Fall 2023

Tue/Thu 11:05 AM – 12:20 PM in Hylan Building, Room 201

Instructor:
Professor Robert Jacobs
Meliora 306
(585) 275-0753

TA: Yifan Li
Wegmans Hall, room 2307

Office Hours

The TA is available to answer students’ questions about the course materials. Students may schedule meetings (either in-person or remote via Zoom) with the TA at times of mutual convenience.

The instructor will stay an extra 5-10 minutes at the end of each class to answer questions. Students may also schedule meetings (either in-person or remote via Zoom) with the instructor at times of mutual convenience.

Course Objectives

The course aims to teach students about probabilistic methods and their applications to understanding human perception and cognition. From a cognitive science perspective, students will learn about current theories of human perception and cognition, and about experimental studies evaluating those theories. How do our brains perceive, think, and act? Why do our brains use some representations and operations as opposed to others? Are the representations and operations used by our brains “optimal” in some sense? From a computer modeling perspective, students will learn about computational methods and issues (borrowed primarily from the fields of artificial intelligence and machine learning).

Prerequisites

The course prerequisites are MATH 161 (Calculus IA) and MATH 162 (Calculus IIA) (or equivalents), and computer programming experience. MATH 164 (Multidimensional Calculus), MATH 165 (Linear Algebra with Differential Equations), and/or STAT 213 (Elements of Probability and Mathematical Statistics) will be helpful, but are not strictly required. Students will need to complete computer programming assignments using the Python programming language (including libraries such as Numpy, Scipy, Matplotlib, etc.).

Attendance and Participation Policy

Class participation is an essential part of this course. A student’s participation credit will be based on both attendance and active involvement in class discussions. If a student must miss a class for a legitimate reason, the student must notify the instructor via email 24 hours in advance and provide documentation to explain the required absence. If a student is unable to attend class due to an unexpected illness or emergency, the student must contact the instructor as soon as possible. Again, the student will need to present appropriate documentation.

Readings

The required textbook for this course is: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action: An Introduction. Cambridge, MA: MIT Press.

There are additional required readings for this course which will be made available via Blackboard.

Brief Reports

Certain classes are designated as “Discussion” classes. For nearly all discussion classes, there are one or more assigned readings. For one or more of these readings (as indicated in the Course Schedule), a student should prepare a “brief report” addressing each of the following questions:

• 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.

It is recommended that brief reports consist of four paragraphs (one paragraph dedicated to each question). Each paragraph should typically contain 1-3 sentences.

Reports must be submitted (PDF files uploaded using Blackboard) before the start of the class on which they are assigned.

Homework Assignments

Three homework assignments will be distributed during the course of the semester. Some assignments ask students to write short essay-style answers to questions, other assignments ask students to solve mathematical problems, and still other assignments ask students to write computer code (using Python) implementing solutions to problems or implementing computational models. Homeworks must be submitted (PDF files uploaded using Blackboard) before 11:05am (the start of class) on the due date.

Exams

The course includes both a midterm exam and a final exam. Exams will use multiple-choice questions. The midterm exam covers all course materials from Classes 1-14. The final exam covers all course materials. Exams are “open note”, meaning that a student may look at printed copies (so-called “hard copies”) of any of the course materials (e.g., lecture notes, readings) during the exam. However, a student cannot look at other materials, cannot use electronic devices, and cannot communicate with anyone while taking an exam.

Final Paper and Presentation

Each student will write a final paper on a topic of their choosing (subject to approval by the instructor). Papers have a maximum length of 1000 words, and should be double-spaced and use a 12 point font. In addition, each student will give a brief (e.g., 5-10 minute) presentation based on their paper toward the end of the semester. Additional instructions will be provided later in the semester.

Due Dates and Exam Dates

All due dates (brief reports, homework assignments, final paper) are firm. We will be strict about this. If a student needs an extension due to a university-sanctioned emergency, the student must notify the instructor via email 24 hours in advance and provide documentation of the emergency. Similarly, exam dates are firm. There will be NO MAKE-UP EXAMS except for university sanctioned emergencies. Again, formal documentation will be required.

Course Grading

• 16%: Class attendance and participation (including brief reports on readings assigned for discussion classes)
• 10%: Homework I
• 10%: Homework II
• 10%: Homework III
• 10%: Final paper and presentation
• 22%: Midterm Exam
• 22%: Final Exam

Collaboration Policy and Academic Honesty

All coursework must be completed by each individual student working alone. Any student suspected of cheating will be referred to the Board on Academic Honest for investigation and possible penalties. Any evidence of collaboration, duplication, or plagiarism (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 Policy.

On the Use of AI Programs

Students may use AI programs (e.g., ChatGPT) to help understand concepts taught in this course. (For example, one might ask an AI program: “I don’t understand the concept of “induction”. Please provide an example of this concept.”.) However, since coding, writing, analytical, and critical thinking skills are part of the learning outcomes of this course, all course assignments must be completed by a student without the assistance of AI programs.

Learning Assistance

Students requiring assistance in learning should contact the AS&E Learning Center at Dewey 1- 154 (phone: 585-275-9049; email: ; CETL website.

Writing Assistance

Students requiring assistance with writing can make an appointment with a writing consultant or fellow at the Writing, Speaking, and Argument Program at Rush Rhees Library G-122 (phone: 585-273-3584; email: ; Writing 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 Office of Disability Resources in Taylor Hall (phone: 585-276-5075; email: ; Office of Disability Resources website

College Course Credit Hour Policy

This course follows the College credit hour policy for four-credit courses. This course meets two times weekly for three academic hours per week. The course also includes independent out-ofclass assignments for at least one academic hour per week. In this course, the independent outof-class assignments include readings of large and/or difficult academic papers and writings of brief reports on several of these papers.