705.615.82 - Artificial Intelligence for Leaders

Course Structure

The course materials are divided into modules which can be accessed by clicking 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

• Introduction to AI
• Machine Learning
• Algorithm Tradeoffs
• Data
• Case Studies
• Resources
• Bias (Human and Machine)
• Responsible AI
• Transformers and Large Language Models
• Symbolic Generative AI
• AI Impacts on Labor
• Designing At-Scale AI Projects
• Managing At-Scale AI Projects
• Student Case Study Presentation and Review

Course Goals

Participants will gain a robust understanding of the foundational and advanced concepts of AI, including the workings of machine learning models, the revolutionary capabilities of transformers and large language models (LLMs), the innovative potential of generative AI, and risk mitigation with symbolic AI. The curriculum emphasizes not only the technical aspects but also the management and ethical considerations, such as bias mitigation and the development of responsible AI frameworks, ensuring leaders can make informed, ethical decisions in deploying AI technologies.

Course Learning Outcomes (CLOs)

• Define common vocabulary, terms, and buzzwords in artificial intelligence (AI).
• Describe the R.O.A.D. framework for scoping and managing large AI projects.
• Explain machine learning performance measures and how to manage performance tradeoffs at scale.
• Explain strengths, weaknesses, and tradeoffs between common AI algorithms.
• Describe common data types and quality considerations.
• Explain sources of human and machine bias.
• Describe applications of generative AI to include transformers, large language models, and symbolic AI.
• Discuss emerging issues of AI impacts on labor.
• Describe a holistic process for managing at-scale AI projects to include risk identification and mitigation.
• Demonstrate sound management practices for at-scale AI programs.

Textbooks

There are no required textbooks for this course. All resources will be accessed online or using eReserves.

Required Software

ChatGPT Plus Plan - Required to purchase a "Plus" plan for assignments. This is the lowest price plan needed for this course.

Technical skills may include:

• Using the learning management system
• Downloading and installing software
• Using presentation and graphics programs
• Using web conferencing tools and software

Digital information literacy skills may include:

• Using computer networks to locate and store files or data
• Analyzing digital information for credibility, currency, and bias (e.g., disinformation, misinformation)
• Properly citing information sources

Student Coursework Requirements

Discussions: Asynchronous weekly discussion boards will provide a forum to collaborate with classmates, ask questions of each other, and share knowledge. Do not post direct answers to assignments or quiz questions, but feel free to discuss concepts. Helpful students will receive bonus points.

Assignments: Each module will contain a graded assignment or quiz that you will complete individually. Weekly assignments will vary depending on module learning objectives. (20% of your total grade)

Quizzes: Each module will contain a graded assignment or quiz that you will complete individually. Weekly assignments will vary depending on module learning objectives. (20% of your total grade)

Midterm Exam: The midterm exam covers modules 1-7, completed during Module 8. (30% of your total grade)

Final Presentation: You will present a final presentation of an AI case study. (20% of your total grade) You will also review and critically evaluate a classmates' final presentation. (10% of your total grade)

Grading Policy

 

Score Range	Letter Grade
100-97	= A+
96-93	= A
92-90	= A−
89-87	= B+
86-83	= B
82-80	= B−
79-77	= C+
76-73	= C
72-70	= C−
69-67	= D+
66-63	= D
<63	= F

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. 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. Our courses are designed with a proactive approach to accessibility to minimize the need for disability disclosure and accommodation requests, but we recognize that you may need additional support. 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 EP Student Disability Services at 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 Student Conduct Code website.

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.