705.605.8VL - Introduction to Generative AI
Description
This introductory course on generative artificial intelligence (AI) offers a comprehensive overview of the foundational principles and techniques that enable machines to generate complex outputs, such as text, images, and music. Students will explore the history and evolution of generative AI, including landmark models and milestones that have shaped the field. The course will first review classical approaches such as expert systems, genetic algorithms, Markov models, and constraint satisfaction problems. This will be followed by key algorithms and models in generative AI, including but not limited to neural networks, Autoencoders, generative adversarial networks (GANs), and transformers. Ethical considerations and societal impacts of generative AI will be a critical component of the curriculum, encouraging students to think critically about issues such as bias, fairness, and privacy. Assessment will include project work, where students will demonstrate their ability to develop a generative AI application from concept to implementation. By the end of the course, students will have a solid understanding of generative AI, equipped with the skills to innovate and apply these technologies in diverse and ethically responsible ways.
Instructors
Erhan Guven
Benjamin Rodriguez
Course Structure
The course materials are divided into 14 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. The modules run for a period of seven (7) days, a wrap-up of each of the modules is contained in the Course Outline listed under the Course Information by clicking Home on the course menu. You should regularly check the Calendar and Announcements for assignment due dates. If you are taking the course in the Summer, please note that Modules 1 and 2 are combined as well as Modules 13 and 14 to ensure all 14 modules are covered during the 12 weeks for the Summer semester.
Course Topics
1 | Introduction | Concepts of generative AI, importance and uses, Discriminative versus Generative AI, Example applications |
2 | Review of ML I | Core probability, statistics, linear algebra, GMM, HMM and Autoregressive algorithms |
3 | Review of ML II | Short review of supervised, unsupervised, and reinforcement learning concepts, review of NN and Variational Auto Encoders |
4 | Classical Generative AI | Genetic algorithms, Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and Bayesian Networks |
5 | Generative Adversarial Networks (GAN) | Adversarial learning, Sample generation, Application on Fashion MNIST dataset |
6 | Advanced GAN Methods and Applications | Deep Convolutional GAN, Wasserstein GAN, Conditional GAN, ProGAN, StyleGAN, and other important GANs |
7 | Mathematical Model of a Transformer | Attention mechanism and Seq2Seq neural network architecture, Transformer model |
8 | Text Generation | Transformer, LLMs, LangChain, evaluation and optimization |
9 | Image and Video Generation | Image synthesis, latent space, Spatio-temporal GANs for video, Optimize and evaluate using inception score (IS) and other metrics |
10 | Audio and Music Generation | Audio synthesis using WaveNet, RNN and LSTM, encoding and decoding musical information, Mel-frequency Cepstral Coefficient (MFCC) and perceptual evaluation methods |
11 | Prompt Engineering | Explicit instructions, context, constraints, prompt design, LangChain |
12 | Retrieval-Augmented Generation (RAG) | Overview of RAG, Retriever and Generator, Evaluation Metrics, LangChain |
13 | Generative Agents | Multiple agents, self-reflection, Chatbots, virtual assistants, creative and simulating agents |
14 | Ethics in AI and Generative Methods | Ethical considerations of creating realistic synthetic media, AI influences, misinformation, intellectual property rights, current regulations |
Course Goals
By the end of this course, you will be able to:
- Master the fundamentals of generative AI and machine learning.
- Implement and apply classical generative AI models, such as Markov models.
- Explore and develop advanced generative models like GANs and their derivatives, and Transformers.
- Adapt the benefits of retrieval-augmented generation (RAG) and prompt engineering to improve generative AI techniques.
- Design and evaluate generative AI applications across various modalities.
- Address ethical considerations and bias in generative AI.
Textbooks
David Foster, Generative deep learning: Teaching Machines To Pain, Write, Compose, and Play, 2nd Edition, O'Reilly Media, Inc., 2023
https://github.com/davidADSP/Generative_Deep_Learning_2nd_Edition
Student Coursework Requirements
- Mini-Discussion-Report Activities (25%)– about 4 over the semester, will run for about 3 weeks each – student generated – students post research/resources for discussion – reports will be produced by student teams
- Formative Quizzes (15%) - Each module will contain a graded quiz. These will assess students’ achievement of the learning objectives in modules - 1 hour timed
Grading Policy
In this course we do not grade on a curve for the final grade.
In this course a +/- grading system is used. The assignments are graded on a curve, the final grade will not be curved or rounded up.
| Score Range | Letter Grade |
|---|---|
| 100-98 | = A+ |
| 97-94 | = A |
| 93-90 | = 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 (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.