- 1A) Use the code given to you to calculate the maximum a posteriori estimates rather than the formulas in the Weiss paper
- 1B) You should have two plots. The x-axes are contrast of second component and size of averaging window and the y-axes for both plots are maximum a posteriori estimates.
- 1C) The parameter set you are manipulating is contrast of 1st component, contrast of second component, angle between components, and speed ratio.
- 2A) "What is the best betting strategy...?" Indicate what is the best prospect (e.g. $4 on A and $12 on B). Provide code that let's me know how you arrived at your answer
- 2B) “Think about the game…” In at least a sentence tell me subjectively how you would bet and why (e.g. I would bet $4 on A and $8 on C because…”
- 2C) "Try to find the best prospect." Same as 2A.
- 2D) “How do they compare” How does the best prospect compare to your subjective decision in 2B? Do you bet the same amounts on the same outcomes? Answer in at least a sentence.
Course Information
Course
description: Mathematical
models of complex human behavior, including individual and group
decision making, information processing, learning, perception, and
overt action. Specific computational techniques drawn from decision
theory, information theory, probability theory, machine learning, and
elements of data analysis.
Topics include: Rational decision making, quantifying costs, rewards, utilities and uncertainty. Individual and group decision making for problems like mate selection. Relationship between decision theory and models for cognition. Learning theory, models of adaptation, elements of machine learning, learning as inference, learning as exploration/
This course is intended for beginning
graduate students and advanced undergraduates. There will be 4-6 homework
assignments and a final project. Grading will be
approximately 60% on the homework assignments, and
40% on the final project. Elliott N119 Secondary: 518 EE/CS
Building
Meetings:
Tuesday and Thursday ,
12:45-2pm,
Professor:
Paul Schrater
E-mail:
schrater AT umn.edu
Office:
Primary: 211 Elliott
Hall,
Office hours:
2-3pm Tues, or by
appointment
TA:
Amy Kalia email: kali0080 AT umn.edu
TA office hrs
Th 11-12, Elliott 309 , or by appointment
Final
Project Assignment:
Your
final project will involve one of the following
1) Simulation or experiments.
2) Literature survey (with critical evaluation) on a given topic.
3) Theoretical work (detailed derivations, extensions of existing work, etc)
In all cases, the work should be written up as a 10-15 page paper. More difficult projects will get better grades if sucessfully completed. You will be evaluated in terms of the care with which you set up and thought through the goals and implementation, and in terms of the competence of the execution. Regardless of form the write up must include a survey of related literature results. This survey counts for 30% of your project grade and should show your ability to independently find, read, understand, and summarize papers in the primary literature related to your project topic.
The project schedule is:
Feb. 24: Topic selection. One or two pages explaining the project with
a list of references.
April 7: Partial report (3 to 5 pages).
May 9: Final report (10 to 15 pages).
Graduate students may be required to give a short presentation of their
project towards the last weeks of the semester.
This presentation will count for 5% of the total class grade (this
grade will be counted as part of the project
grade).
Syllabus
PSY 5018H Syllabus
Policies
Grading will be approximately 60% on the homework assignments, 40% final project. Late homework is not accepted without serious and documented excuse.Cheating and Plagiarism
The homework and programming
assignments
must not be the result of cooperative work. Each student
must work individually in order to understand the material in depth.
You may discuss the issues but by
no means, copy the homework or the programming assignment of somebody
else. All work in the projects
and the programming assignment must properly cite sources. For example,
if you quote a source in your
project, you must include the quotation in quotation marks and clearly
indicate the source of the quotation.
Any student caught cheating will receive an F
as a class grade
and the University policies for cheating
and plagiarism will be followed.
Reading Materials
All reading material will be in the
form of
papers or book chapters that students will download from this web
site. No textbook currently exists that covers the intended range of
subject
matter. Please note that some of the primary literature will be
difficult and written for an expert audience rather than for
instruction.
While there is no required text, books that I may draw from and that cover some of the material at a more introductory level include:
for programming statistics in Matlab see:
Computational Statistics Handbook (Matlab software)
Some concepts from linear algebra are extremely helpful: