Intelligent algorithms are, in many cases, practical alternative techniques for tackling and solving a variety of challenging engineering problems. For example, fuzzy control techniques can be used to construct nonlinear controllers via the use of heuristic information when information on the physical system is limited. Such heuristic information may come, for instance, from an operator who has acted as a "human-in-the-loop" controller for the process. This course investigates several concepts and techniques commonly referred to as intelligent algorithms; discusses the underlying theory of these methodologies when appropriate; and takes an engineering perspective and approach to the design, analysis, evaluation, and implementation of Intelligent Systems. Fuzzy systems, genetic algorithms, particle swarm and ant colony optimization techniques, and neural networks are the primary concepts discussed in this course, and several engineering applications are presented along the way. Expert (rule-based) systems are also discussed within the context of fuzzy systems. An intelligent algorithms research paper must be selected from the existing literature, implemented by the student, and presented as a final project. Prerequisite(s): Student familiarity of system-theoretic concepts is desirable.
The course materials are divided into modules which can be accessed by clicking Course Modules on the course menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.
To introduce the student to the theory, design and analysis of Intelligent Systems from an (applied) engineering perspective; the primary technical emphasis of the course is on fuzzy systems, genetic algorithms, particle swarm and ant colony optimization techniques, and neural networks. Traditional gradient-based optimization techniques are also introduced as off-line design tools and for on-line adaptation of existing Intelligent Systems designs; this is done to provide the student with a baseline understanding as compared with Intelligent Systems methods like Genetic Algorithms, Particle Swarm and/or Ant Colony Optimization Techniques. The link between fuzzy systems and neural systems is also highlighted.
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, by Jang/Sun/Mizutani, Pearson Publishers.
The out-of-print textbook, above, is available online through the appropriate bookstore website, or directly via the URL: https://store.cognella.com/24654.
You will need access to a recent version of MATLAB. The MATLAB Total Academic Headcount (TAH) license is now in effect. This license is provided at no cost to you. Send an email to email@example.com to request your license file/code. Please indicate that you need a standalone file/code. You will need to provide your first and last name, as well as your Johns Hopkins email address. You will receive an email from Mathworks with instructions to create a Mathworks account. The MATLAB software will be available for download from the Mathworks site.
Preparation and Participation (10% 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.
Post your initial response to the discussion questions by the evening of day 3 for that module week. Posting a response to the discussion question is part one of your grade for module discussions (i.e., Timeliness).
Part two of your grade for module discussion is your interaction (i.e., responding to classmate postings with thoughtful responses) with at least two classmates (i.e., Critical Thinking). Just posting your response to a discussion question is not sufficient; we want you to interact with your classmates. 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. In some instances, I/we will summarize the overall discussions and post the summary for the module.
Evaluation of preparation and participation is based on contribution to discussions.
Preparation and participation is evaluated by the following grading elements:
Preparation and participation is graded as follows:
Assignments (20% 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. Include a cover sheet with your name and assignment identifier. Also include your name and a page number indicator (i.e., page x of y) on each page of your submissions. Each problem should have the problem statement, assumptions, computations, and conclusions/discussion delineated. All Figures and Tables should be captioned and labeled appropriately.
All assignments are due according to the dates in the Calendar.
Late submissions will be reduced by one letter grade for each week late (no exceptions without prior coordination with the instructors).
If, after submitting a written assignment you are not satisfied with the grade received, you are encouraged to redo the assignment and resubmit it. If the resubmission results in a better grade, that grade will be substituted for the previous grade.
Qualitative assignments are evaluated by the following grading elements:
Qualitative assignments are graded as follows:
Quantitative assignments are evaluated by the following grading elements:
Quantitative assignments are graded as follows:
Course Project (20% of Final Grade Calculation)
As part of this course, the student will research and select (pending instructor approval) a journal article discussing an intelligent system application in an area of particular interest to the student. Example applications areas include: fuzzy logic control of an autonomous car; fuzzy logic anti-skid braking systems; genetic algorithms applied to the design of radar antenna systems; genetic algorithm image processing applications; ant colony optimization applied to solving the traveling salesman problem; etc. Purely theoretical / non-applied papers are not allowed. Once an applied application paper is approved by the instructor, the student researches the paper along with any (relevant) associated articles, implements the concept in computer simulation (e.g., within the Matlab environment) to replicate the author’s publishes results, and develops a final presentation for the class. In this way, each student gains deeper exposure to an application are of interest, and the entire class gains a broad exposure to many different application areas via the student presentations.
Students are encouraged to begin researching project topics within the first week or two of the semester start date, and all students should have their papers approved by the instructor by Module 7. To get started, the student should select 3-5 potential published papers to choose from; select papers based on readability, clarity and repeatability of the published results, and your particular interests. Student presentations are due at the end of Module 13.
Student final presentations should include the following sections:
Additional information and detailed instructions about the course project can be found in the “Course Project Description” document located within the Course Information folder of the course site.
The course project is evaluated by the following grading elements:
Course Project is graded as follows:
Quizzes (20% of Final Grade Calculation, combined from 10% for Quiz 1 and 10% for Quiz 2)
Quiz #1 will be available at the start of Module 4 and Quiz #2 will be available at the start of Module 11. You can take the quiz at any time during the week they are released. However, the quizzes are self-timed and you will have 30-minutes to complete them once you have viewed / downloaded them. Once you have completed the quiz, scan the quiz with your solutions into PDF form (make sure the scan is oriented correctly and is readable) and upload the result to the assignment area. Do not discuss the quiz with anyone, including your classmates until quizzes are graded. You may refer to a 1-sided sheet of your notes / equations, and you may not open the textbook or search the internet for answers.
The quizzes are evaluated by the following grading elements:
Quizzes are graded as follows:
Exams (30% of Final Grade Calculation, combined from 15% for Midterm and 15% for Final)
The midterm exam will be available at the start of Module 8 and the final exam will be available at the end of Module 14. As with the quizzes, you can take the exams at any time during the week they are released. However, the exams are self-timed and you will have 60-minutes to complete the midterm and 75-minutes to complete the final exam once you have viewed / downloaded them. Once you have completed the exam, scan the exam with your solutions into PDF form (make sure the scan is oriented correctly and is readable) and upload the result to the assignment area. Do not discuss the exam with anyone, including your classmates until the exams are graded. You may refer to a 1-sided sheet of your notes / equations, and you may not open the textbook or search the internet for answers.
The exams are evaluated by the following grading elements:
Exams are graded as follows:
Assignments are due according to the dates posted in your Canvas course site. You may check these due
dates in the Course Calendar or the Assignments in the corresponding modules. I will post grades one week after
assignment due dates.
I generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English
language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as
an indication of poor written communication ability that may detract from your grade.
A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week.
A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.
EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).
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 firstname.lastname@example.org.
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, email@example.com.
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/
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).
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.