525.670.81 - Machine Learning for Signal Processing

Course Structure

The course materials are divided into modules which can be accessed by clicking Course Modules on the left menu. A module will have several sections including the overview, content, readings, quizzes, 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.  Modules start on Wednesdays of the week.  To accommodate student’s schedule conflicts due to work-related assignments, the module will be available two weeks (14 days) prior to the module end date.  If this arrangement is still hard to fit in student’s work schedule, I recommend that the student contemplate taking this course at another time.  Students should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

• Introduction to Machine Learning Signal Processing
• Fundamentals of Linear Algebra
• Optimization
• Representation / Deterministic Representations
• Data-driven Representations: ICA
• Non-negative Matrix Factorization Data Representation
• Clustering
• Regression and Classification
• Bayes Classification: Bayesian Neural Network
• Statistical Modeling: Linear Gaussian Models
• Linear Gaussian Models (LGM)
• DTW and Hidden Markov Models
• Neural Networks
• Modeling / Representation / Classification / Prediction: Neural Network
    

Course Goals

To focus on the application of machine learning theory and algorithms to model, classify, and retrieve information from different kinds of real-world signals such as audio, speech, image, and video.

Course Learning Outcomes (CLOs)

• • Describe the overall objective of Optimization
• • Compute Eigen decomposition of correlation matrix and select Eigen vectors
• • Explain Gaussian classifier and Gaussian Mixture Model
• • Describe Multi-layer Perceptron, Bayesian Neural Network, and Convolutional Neural Networks

Textbooks

Required

Alpaydin, E. (2020) Introduction to Machine Learning (4th ed.). Cambridge MA: The MIT Press. ISBN-13: 978-0262043793

Bishop, C.M. (2006) Pattern Recognition and Machine Learning. New York, NY: Springer Science + Business Media, LLC.  ISBN-13: 978-0387310732 

Textbook information for this course is available online through the appropriate bookstore website: For online courses, search the MBS website at http://ep.jhu.edu/bookstore.

Required Software

MATLAB:

The software application MATLAB will be used throughout this course. Many students may have access to MATLAB through their place of employment. For those that don’t, purchasing the Student Version of MATLAB through the Mathworks web site might be beneficial. The Student Version is available for Windows, Mac, and Linux operating systems and includes many Toolboxes that are useful for specific application domains. Visit http://www.mathworks.com/academia/student_version/ for specific details.

Alternatively, MATLAB is available via the JHU Total Academic Headcount (TAH) license. This license is provided at no cost to you. Send an email to software@jhu.edu 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 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.

Student Coursework Requirements

It is expected that each module will take approximately 7–11 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 hours per week), and completing homework assignments (approximately 3–5 hours per week).

This course will consist of the following basic student requirements:

Preparation and Participation (20% 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 4 (Saturday) for that module week. Posting at least one topic to the discussion question is Part One of your grade for module discussions (i.e., Timeliness).  The discussion rubric will be attached to serve as the guidance for preparing the discussion post.

Part Two of your grade for module discussion is your interaction (i.e., responding to classmate postings with thoughtful responses) with at least two classmate’s topics (i.e., Critical Thinking). Just posting your response to a discussion question would not be 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 as discussions are posted.

Evaluation of preparation and participation is based on contribution to discussions.  The grade will be posted within a week so that you will know where you are in this category.  I may provide feedback along with the grade.

Preparation and participation are evaluated by the following grading elements:

1. Timeliness (50%)
2. Critical Thinking (50%)

Quizzes (15% of Final Grade Calculation)

Quizzes are graded based on the correctness of the answer.  The points of each question are shown at the end of each question.  The questions may be True/False or multiple-choice or explanations to your response. At the end of the semester, the percentage of the points earned out of the total possible points of the quizzes will be calculated.  When you take the quizzes, it is recommended that you have reviewed the modules.  During the quizzes, if you have any doubt how to answer the questions, you save the session and check the relevant information needed and come back to complete the quizzes.  If the score of the quiz is below what to be expected, upon instructor’s discretion, the student may be offered an option to re-take the quiz.  The average of the two scores will be used as your final score. 

To embrace the principles of active learning, I will solicit quiz questions from students, who are required to submit a quiz question per module with an expected answer.  The quiz question SHALL BE a True/False or multiple-choice question, NOT an essay question.  The questions need to be based on the content covered in the module, including the linked references.  If the submitted questions are selected to be used in future quizzes (the spaced and interleaved testing), bonus points will be credited.  Through this self-quiz exercise, students will be benefited from the self-reflection on the subject just learned in the module.  

Assignments (35% of Final Grade Calculation)

Assignments will include a mix of quantitative and qualitative assignments. 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. Specific instructions of each assignment will be included in each homework assignment.

All assignments are due according to the dates in the Calendar.  If students have any personal matters such that they will not be able to meet the deadline for the assignment submission.  Please inform the instructor prior to, not after, the due date.  Special arrangements can be made to accommodate the situation. 

Late submissions will be reduced by one letter grade for each week late (no exceptions without prior coordination with the instructors).  No late submissions will be accepted after Week 13.  

If, after submitting a written assignment the grade received is below 50%, at instructor’s discretion, you may be offered an option to redo the assignment and resubmit it for grading. The average of the two scores will be used as your final score. 

Qualitative assignments are evaluated by the following grading elements:

1. Each part of question is answered (20%)
2. Writing comments in the submitted MATLAB / Python scripts (30%)
3. Rationale is included in the comment (20%)
4. Output is clearly presented, either with the printout or the graphics (15%)
5. Outside references are included (15%)

Quantitative assignments are evaluated by the following grading elements:

1. Each part of question is answered (20%)
2. Assumptions are clearly stated (20%)
3. Intermediate derivations and calculations are provided (25%)
4. Answer is technically correct and is clearly indicated (25%)
5. Answer precision and units are appropriate (10%)
6. Self-refection essay needs to be written in MS Word and Math equation derivations need to be written by using MS Word Equation Editor. Manual transcripts are not acceptable. 

Course Project (30% of Final Grade Calculation)

Students will work on the course project as a group.  The group assignment will be announced in the first week of the semester.  The schedule (from the proposal submission, project execution and closeout presentation) will be released in the first week of the class. In Week 13, each team will review the results of other team’s projects and submit comments on each of them.  A final report of the team project is due at the end of the semester.

The course project is evaluated by the following grading elements:

1. Project group proposal (15%)
2. Project mid-term review. (15%)
3. Project presentation. (30%)
4. Project report (including Results and Analysis) (30%)
5. Discussions on other team’s projects (10%)

Grading

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.

We 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.

The final grade of this course will be determined by the following weighting:

Grading Policy

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).

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.