605.740.81 - Machine Learning: Deep Learning

Course Structure

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.

Course Topics

• Deep Learning Basics
  
• Convolutional Neural Networks
• Training Neural Networks
• Architectures
• Sequence Modeling
• Unsupervised Learning
• Generative Models
• Advanced Topics
  • Generalization, domain gaps, and explainable AI
  • Domain gaps and black boxes
  • Character and word embedding
  • Natural Language Processing and Transformers
  • Deep Reinforcement Learning
• Human-centered AI, ethics

Course Goals

Specific Outcomes for this course are that:

• Students will learn fundamental concepts of machine learning
• Students will learn the theoretical underpinnings of deep learning
• Students will learn contemporary architectures, applications, and challenges of deep learning
• Students will learn to design, implement, and validate deep learning-based solutions to machine learning problems

Course Learning Outcomes (CLOs)

• Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
• Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
• Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
• Apply computer science theory and software development fundamentals to produce computing-based solutions.

Textbooks

This course does not explicitly follow a specific textbook, though many good textbooks on the topic are available. Additional reading material (including textbooks, blog articles, tutorials, and scientific articles) will be made available if need be.

Required Software

Python and Jupyter Notebook is required to complete most of the assignments. Latex, Texmaker, or Overleaf is recommended for assignments and project.

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).

Everything in this class should be typed or programmed there will be no handwritten submissions accepted and will result in a score of zero on that work.

Any file and or documentation should include your statement of integrity on the cover page as shown below:

Statement of Integrity: I [your name], have prepared this document and or file to the best of my ability without academic misconduct that adheres to the standards and guidelines outlined by my professor and university. I have prepared this work without external resources (large language models, online and offline solutions, and any other resources that can be seen as violating academic dishonesty) unless asked or stated otherwise by my professor. Anything that is seen violating this statement of integrity will result in a score of zero on that work.

This course will consist of the following basic student requirements:

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 3% for each day late (no exceptions without prior coordination with the instructors).

Written Assignments (25% of Final Grade Calculation)

There are 3 Written Assignments (Assignments 1, 3, and 5) that are a little more theoretical and mathematical in nature so that the concepts are understood prior to the Programming Assignments.

Qualitative assignments are evaluated by the following grading elements:

1. Each part of question is answered (20%)
2. Writing quality and technical accuracy (30%) (Writing is expected to meet or exceed accepted graduate-level English and scholarship standards. That is, all assignments will be graded on grammar and style as well as content.)
3. Rationale for answer is provided (20%)
4. Examples are included to illustrate rationale (15%) (If you do not have direct experience related to a particular question, then you are to provide analogies versus examples.)
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%)

Programming Assignments (25% of Final Grade Calculation)

There are 3 Written Assignments (Assignments 2, 4, and 6) that are applied and will consist of material and work you have covered in this class. You are expected to generate a report and code to answer the questions to get full credit.

Qualitative assignments are evaluated by the following grading elements:

6. Each part of question is answered (20%)
7. Writing quality and technical accuracy (30%) (Writing is expected to meet or exceed accepted graduate-level English and scholarship standards. That is, all assignments will be graded on grammar and style as well as content.)
8. Rationale for answer is provided (20%)
9. Examples are included to illustrate rationale (15%) (If you do not have direct experience related to a particular question, then you are to provide analogies versus examples.)
10. Outside references are included (15%)

Quantitative assignments are evaluated by the following grading elements:

6. Each part of question is answered (20%)
7. Assumptions are clearly stated (20%)
8. Intermediate derivations and calculations are provided (25%)
9. Answer is technically correct and is clearly indicated (25%)
10. Answer precision and units are appropriate (10%)

Research Project (30% of Final Grade Calculation)

There is 1 Research Project that you will conduct with a group consisting of your classmates. Further information on the work and organization will be elaborated on by the instructor and class as you progress through the semester.

A course project will be assigned throughout the course. The next-to-the-last week will be devoted to the course project.

The course project is evaluated by the following grading elements:

1. Student preparation and participation (as described in Course Project Description) (40%)
2. Student technical understanding of the course project topic (as related to individual role that the student assumes and described in the Course Project Description) (20%)
3. Team preparation and participation (as described in Course Project Description) (20%)
4. Team technical understanding of the course project topic (as related to the Customer Team roles assumed by the students and the Seller Team roles assumed by the students and described in the Course Project Description) (20%)

Discussions (20% of Final Grade Calculation)

Discussions span every two weeks. The first week you are asked to answer prompt questions and the sencon week you will be asked to respond to your group members about there responses to that same prompt. Be constructive and pride feedback. You can use your prior experience, online references and chat with each other in the discussion to come up with a solution and thought provoking conversation.

You are responsible for carefully reading all assigned material and being prepared for discussion. The majority of readings are from the course videos and slides. Additional reading may be assigned to supplement text readings.

Post your initial response to the discussion questions by the end of the first 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 3+ posts (i.e., Critical Thinking). Just posting your response to a discussion question is not sufficient; I 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 will summarize the overall discussions and post the summary for the module.

Discussion grades are evaluated by the following grading elements:

• Timeliness (50%)
• Critical Thinking (50%)

Grading Policy

Assignments are due according to the dates posted on the canvas course site. You may check these due dates in the Course Calendar or the Assignments 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.

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).

Final grades will be determined by the following weighting:

Course Policies

The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful, abiding by the Computer Science Academic Integrity Policy:  

Cheating is wrong. Cheating hurts our community by undermining academic integrity, creating mistrust, and fostering unfair competition. The university will punish cheaters with failure on an assignment, failure in a course, permanent transcript notation, suspension, and/or expulsion. Offenses may be reported to medical, law or other professional or graduate schools when a cheater applies. 

Violations can include cheating on exams, plagiarism, reuse of assignments without permission, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition. Ignorance of these rules is not an excuse.

Academic honesty is required in all work you submit to be graded. Except where the instructor specifies group work, you must solve all homework and programming assignments without the help of others. For example, you must not look at anyone else’s solutions (including program code) to your homework problems. However, you may discuss assignment specifications (not solutions) with others to be sure you understand what is required by the assignment.

If your instructor permits using fragments of source code from outside sources, such as your textbook or on-line resources, you must properly cite the source. Not citing it constitutes plagiarism. Similarly, your group projects must list everyone who participated.

Falsifying program output or results is prohibited. 

Your instructor is free to override parts of this policy for particular assignments. To protect yourself: (1) Ask the instructor if you are not sure what is permissible. (2) Seek help from the instructor, TA or CAs, as you are always encouraged to do, rather than from other students. (3) Cite any questionable sources of help you may have received. 

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.