705.742.8VL - Advanced Applied Machine Learning

Expanded Course Description

Important: A computer with a recent CUDA-capable GPGPU is highly recommended. Several course topics provide examples of large neural networks, such as Large Language Models and Generative AI.

Course Structure

The course materials are divided into modules, one for each week of the course. All course materials and assignments will be housed in Canvas and Microsoft Teams. The module content can be accessed by clicking Course Modules on the left 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

• Recommender Systems
• Explainable AI
• Hyperparameter Optimization
• Advanced Feature Extraction
• Generative Adversarial Networks
• Recurrent Neural Networks
• Time Series and Sequence Learning
• Attention Neural Networks
• Transfer Learning
• Word Embeddings
• Transformers and Large Language Models
  
• Graph Neural Networks
• Machine Learning Operations
• Deep Reinforcement Learning

Course Goals

• Analyze to measure and discover the "right features" via classical feature extraction techniques, and advanced techniques of Sparse Coding, and Neural Networks.
• Build transfer learning pipelines to demonstrate feature transfer, pre-trained models, and few shot learning.
• Formulate and build a variety of neural networks designed to address sequential and timeseries data, such as disease, cybersecurity, and natural language.
• Adapt model analysis and machine learning operations methods to compare and contrast trained machine learning models in terms of their accuracy, explainability, and limits.
• Research recent machine learning topics and emerging methods in explainable AI, and neural networks. Assess merit of emerging methods and their alignment to advanced methods covered in the course.

Course Learning Outcomes (CLOs)

• Adapt model analysis and machine learning operations methods to compare and contrast trained machine learning models in terms of their accuracy, explainability, and limits.
• Analyze to measure and discover the "right features" via classical feature extraction techniques, and advanced techniques of Sparse Coding, and Neural Networks.
• Build transfer learning pipelines to demonstrate feature transfer, pre-trained models, and few shot learning.
• Formulate and build a variety of neural networks designed to address sequential and timeseries data, such as disease, cybersecurity, and natural language.
• Research recent machine learning topics and emerging methods in explainable AI, and neural networks. Assess merit of emerging methods and their alignment to advanced methods covered in the course.

Textbooks

Raschka, Sebastian, et al. Machine Learning with PyTorch and Scikit-Learn. Packt Publishing Ltd, 2022. ISBN-13: 978-1801819312.

Student Coursework Requirements

It is expected that each module will take approximately 10–16 hours per week to complete. Here is an approximate breakdown:

• Attending physical or Virtual Live classroom sessions (3 hours per week)
• Reading the assigned sections of the texts (approximately 1–2 hours per week) as well as some outside reading
• Completing assignments (approximately 4–8 hours per week)
• Researching the paper topic and engaging in weekly discussions (approximately 2–3 hours per week)

This course will consist of the following basic student requirements:

Class Participation (10% of Final Grade Calculation)

You are responsible for carefully reading all assigned material and being prepared for both the Virtual Live classroom sessions and Discussions. The majority of readings are from the course text. You will be responsible for all assigned reading material, whether we cover it in class or not, so prepare questions about parts of the readings not understood. There may also be optional readings recommended from the archival literature.

Discussions (20% of Final Grade Calculation)

An important part of your course grade is participating in Discussions with your classmates. Discussions will be held over the course of three weeks and will be conducted in Microsoft Teams, with a separate channel for each Discussion. For each Discussion, you will be assigned to a group and the groups will change for each three-week Discussion so that you will eventually get to know all of your classmates.

Post your initial response to the discussion questions by the evening of Day 7 for the first week of the Discussion (Modules 1, 4, 7, and 10). 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 your classmates (i.e., Critical Thinking). This should be completed by the evening of Day 7 for the second week of the Discussion (Modules 2, 5, 8, and 11). 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.

In addition, you should ask questions and answer your classmates’ questions as part of the Discussion grade. Asking questions is one of the best ways to better understand the course content. I will do my best to answer the questions you post, but your classmates may be able to give you an answer more quickly than I can!

Part three of your grade for module discussion is the group report (i.e., a whitepaper or a report assembling your findings and conclusions as a group with your classmates). This should be completed by the evening of Day 7 for the third week of the Discussion (Modules 3, 6, 9, and 12). Reports will be posted in Teams in PDF format.

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.

Discussions are evaluated by the following grading elements (please check the Discussion Rubric):

• Timeliness (30%)
• Cross-Section of Posting (35%)
• Critical Thinking (35%)

Research Paper (30% of Final Grade Calculation)

The research paper will involve the theoretical and experimental analysis of a problem/solution using advanced machine learning methods. I will provide a list of applications to be selected from (not exclusively, as students may bring their own). Students are expected to develop a project, which has the introduction to the problem/solution/program, the mathematical analysis, specification, verification, experimental setup, scenarios, and conclusions. The script may be acquired from other sources provided that references are given and the script is "made your own". i.e. similar to writing a research paper. Please check the course Project Rubric.

Assignments (40% of Final Grade Calculation)

Assignments will include real-world problems. Although the Assignments will usually reflect the current material, I will also give on occasion a brain-building problem that may no direct relation to the material but rather may require basic logical reasoning to solve.

Assignments are assigned more-or-less every week and can involve basic materials, further examination of concepts introduced and presented in class and in the textbook, brainteasers, and more challenging questions problems. Problems will be the basis for class discussions as well; be prepared to ask and answer questions and discuss the problems.

Any course materials prepared for evaluation for grades must be turned in Canvas in .ipynb format (Jupyter Notebooks). All assignments are due according to the dates in the Calendar. If there is a legitimate reason why an assignment is going to be late and this can be known in advance (i.e., excluding illness) then the instructor must be notified of such. Illness is a legitimate excuse for lateness but please let me know as soon as possible and we can make arrangements for delivery. NO ASSIGNMENTS WILL BE ACCEPTED IF SUCH NOTIFICATION WAS NOT MADE.

Grading Policy

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.