605.647.81 - Neural Networks

Expanded Course Description

This course provides an introduction to many concepts involving neural networks.  Topics include modeling biological nerve cells, how that inspires mathematical models of neurons known as perceptrons, review of mathematical tools for the analysis of perceptrons, perceptron modeling and computation of logic statements.  The mathematics of supervised learning techniques for perceptrons and multilayer neural networks are also covered.  Further topics include convolutional neural networks, metaheuristics, recurrent neural network architectures, unsupervised learning and memory capacity of recurrent networks, and stochastic versions of recurrent networks such as Restricted Boltzmann Machines.  Hebbian learning and competitive learning and self-organizing maps are also reviewed.   Finally, cellular neural networks are discussed as well as state-of-the-art research and techniques.

Course Structure

The course materials are divided into 14 modules each roughly corresponding to a week of study for the course.  These modules can be accessed by clicking Course Modules on the left menu of the Blackboard website page and will typically be comprised of several components.  These components include

• a Module Content page with general instructions on how to complete the module;
• links to video lecture materials;
• links to online quizzes;
• links to reading assignments and indications of required reading in the text;
• links to pdf formatted module assignments;
• discussion questions.

Students are encouraged to preview all sections of the module before starting.  Most modules run for a period of seven (7) days, exceptions are noted on the Course Outline page.  Students should regularly check the Calendar and Announcements for assignment due dates and any changes or modifications of the course.

Course Goals

By the end of the course, students will be able to:

• Explain the biological and mathematical foundations of neural network models.
• Explain the different types of neural networks and different types of learning models.
• Develop mathematical competence for understanding neural networks,
• Explain which types of neural networks are used for which purposes such as in discriminators, classifiers, computation, and a broad range of problems.
• Explain how neural networks are implemented using training algorithms such as the feed-forward, back-propagation algorithm.
• Use computational tools for experimentation leading to new theoretical insights.
• Design, build and train neural networks for practical purposes.

Textbooks

Required

Neural Networks - A Systematic Introduction, by Raul Rojas downloadable from http://page.mi.fu-berlin.de/rojas/neural/

Other Materials & Online Resources

Optional As noted above, additional reading assignments and materials will be provided in the various Course Content pages either as links to download pdf files or links to other websites.

Required Software

Students must have some facility using a computer programming language to solve various module  programming assignments.  Programming languages such as Java, C#, C++, C, Python or programming environments such as MATLAB, Octave, Scilab and FreeMat are also satisfactory.

Student Coursework Requirements

Student assignments are due according to the dates in the Calendar and Assignments items in the corresponding modules.

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.

Final grades will be determined using the following weighting:

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.