605.745.01 - Reasoning Under Uncertainty
Description
This course is concerned with the problems of inference and decision making under uncertainty. It develops the theoretical basis for a number of different approaches and explores sample applications. The course discusses foundational issues in probability and statistics, including the meaning of probability statement, and the necessity of a rational agent acting in accord with probability theory. We will look at possible generalizations of Bayesian probability, including Dempster-Shafer theory. Next, we will develop algorithms for Bayesian networks—graphical probabilistic models—for exact and approximate inference and consider several application areas. Finally, the course will examine the problem of making optimal decisions under uncertainty. We will explore the conceptual foundations of decision theory and then consider influence diagrams, which are graphical models extending Bayesian networks to the domain of decision analysis. As time permits, we will also look at Bayesian games and Markov decision processes. Pertinent background in probability and theoretical computer science is developed as needed in the course.
Instructor
Marc Johnson
Course Structure
Please see the Course Outline in the Canvas Course Site: EN.605.745.01.SP24 Reasoning Under Uncertainty
Course Topics
- The foundations of probability
- Mathematical Formulation and Interpretations
- Mathematical Formulation and Interpretations
- Reasoning with Imprecise Probabilities
- Belief functions and other formalisms
- Bayesian Networks
- Exact and Approximate Inference
- Utilities and Decisions
- Decision Trees and Influence Diagrams
- Possibility Theory
- Fuzzy Logic
Course Goals
- Identify situations where Bayesian Inference would be useful and implement it with a programming language.
- Evaluate the effectiveness of other reasoning frameworks in relation to a problem.
- Model expert systems with a suitable imprecise probability framework.
Course Learning Outcomes (CLOs)
- Summarize the interpretations of probability.
- Explain the axioms of probability.
- Review joint probability distributions.
- Create belief functions for uncertain reasoning.
- Evaluate probability trees for statistical reasoning.
- Analyze various models of reasoning under uncertainty.
- Explain fuzzy logic framework.
- Construct Bayesian networks.
- Perform Bayesian inference with various algorithms.
- Model expert systems.
- Calculate probabilities with possibility measures.
- Create Decision Trees and Influence Diagrams.
- Define methods for handling imprecise probability.
Textbooks
Koller, D. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press
Other Materials & Online Resources
Shafer, G. (1976) A Mathematical Theory of Evidence. Princeton University Press.
Student Coursework Requirements
It is expected that each module will take approximately 7–10 hours per week to complete. Here is an approximate breakdown: reading the assigned sections of the texts (approximately 3–4 hours per week) as well as some outside reading, listening to the audio annotated slide presentations (approximately 2–3 hours per week), and writing assignments (approximately 2–3 hours per week).
This course will consist of the following basic student requirements:
Discussion Boards (12% of Final Grade Calculation)
You are responsible for carefully reading all assigned material and being prepared for discussion. The majority of readings are from the course text. Additional reading may be assigned to supplement text readings.
The discussion board will consist of 12 distinct discussions. They should be a way to interact online with other students. Be detailed in your postings and in your responses to your classmates' postings. Feel free to agree or disagree with your classmates. Please ensure that your postings are civil and constructive.
I will monitor module discussions and will respond to some of the discussions as discussions are posted.
Homework Assignments (16% of Final Grade Calculation)
Assignments will include a mix of qualitative assignments (e.g. literature reviews, model summaries), quantitative problem sets, and case study updates.
All assignments are due according to the dates in the Calendar. There will be six homework assignments and each will be weighted the same. The Problem Sets will differ in how many problems they have but the total weight will be the same, 16% of your final grade.
Refer to assignment criteria in Canvas for a breakdown of how each will be graded.
Course Project (38% of Final Grade Calculation)
A course project will be assigned upon the start of the course. There will be interim deliverables throughout the semester pertaining to the course project. There will be six interim deliverables:
Define Research Problem – 4%
Skeleton of Research Paper – 4%
Rough Draft of Operable Code - 4%
Rough Draft of Paper – 4%
Project Presentation – 9%
Final Research Paper – 13%
Refer to the Canvas Project Information page for a breakdown of how deliverables are graded.
Quizzes (14% of Final Grade Calculation)
There will be weekly quizzes available in each Module. You will have one week to complete each quiz and they will be due by Midnight exactly one week from their release. You may use the course text to complete the quizzes. There will total 14 quizzes, each contributing 1% to your total grade in the course. The quizzes are content based and should not be time consuming or convoluted. If you have any confusion, please ask the professor so that the content can be completely understood before we end the module.
Refer to Canvas for breakdown of how quizzes are graded.
Final Exam (20% of Final Grade Calculation)
The final exam will be available in the last Module. You will have one week to complete the exam and they will be due by 5PM exactly one week from the release. You may use the course text to complete the exam.
Refer to Canvas for a breakdown of how the Final will be graded.
Grading Policy
Grading will be a weighted sum of performance on all assignments during the semester. The weightings are described below. The grades will follow the standard EP grading scale as shown.
| Score Range | Letter Grade |
|---|---|
| 100-98 | = A+ |
| 97-94 | = A |
| 93-90 | = A− |
| 89-87 | = B+ |
| 86-83 | = B |
| 82-80 | = B− |
| 79-77 | = C+ |
| 76-73 | = C |
| 72-70 | = C− |
| 69-67 | = D+ |
| 66-63 | = D |
| <63 | = F |
Final grades will be determined by the following weighting:
| Item | % of Grade |
|---|---|
| Discussion Board | 12% |
| Homework Assignments | 16% |
| Quizzes | 14% |
| Project | 38% |
| Final Exam | 20% |
Course Policies
Personal Wellbeing
If you are struggling with anxiety, stress, depression or other mental health related concerns, please consider connecting with the Johns Hopkins Student Assistance Program (JHSAP). If you are concerned about a friend, please encourage that person to seek out our services. JHSAP can be reached at 443-287-7000 or https://jhsap.org/
Tutoring Website
Johns Hopkins Engineering for Professionals offers a tutoring connection network that allows students to connect with other Johns Hopkins Engineering students or alumni for tutoring services. This service allows students to search a list of courses to “Find a Tutor” or complete a profile to “Become a Tutor.” More information about this service can be found on the tutoring website (https://tutor.ep.jhu.edu/)
Academic Policies
Deadlines for Adding, Dropping and Withdrawing from Courses
Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.
Academic Misconduct Policy
All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.
This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.
Students with Disabilities - Accommodations and Accessibility
Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.
For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.
Student Conduct Code
The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.
For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/
Classroom Climate
JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).
Course Auditing
When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.