Special topics courses provide an opportunity for in-depth study of topics not offered elsewhere and of topics of current significance.
- CIS4930 for undergraduate students
- CIS6930 for graduate students
Brief descriptions and expected prerequisites can be found below.
CIS4930/CIS6930 Machine Learning for Networks, Cloud and Communication Systems
Instructor: Ye Xia
Expected background: Prior knowledge about machine learning is desirable but not necessary. Strong math skills at the level of typical CS and engineering programs, e.g., calculus, probability and statistics, and linear algebra.
Description: We will investigate the applications of machine learning to computer and communication systems, including networks and cloud. We will focus our attention on how to use machine learning techniques to solve difficult resource-allocation or optimization problems frequently encountered in these application areas. The course content will be a combination of learning some basic machine learning techniques, including neural networks, and studying research papers in the application areas.
CIS4930 Introduction to Machine Learning
Instructor: Al-Saad, Mohammad
Expected background: Proficiency in a programming language (Python) Linear Algebra, Calculus
Description: Machine learning is a specialized area within artificial intelligence focused on enabling computer programs to autonomously enhance their functionality and efficiency by acquiring (learning) experience. The primary goals of this course are to equip students with a comprehensive introduction to machine learning methods and techniques, and to delve into the investigation of research problems within machine learning and its applications, which may lead to work on a project or a dissertation. The course is intended primarily for computer science and artificial intelligence students. Additionally, students from various fields who possess a keen interest and a robust background in artificial intelligence may also find this course interesting.
CIS6930 Blockchain Course:
Instructor: My Thai
Description: Blockchain has been emerged as one of the World’s most trusted and decentralized system, which will have an immense global impact similar to the way Internet did in the mid 90’s. The potential applications of Blockchain technologies span across a variety of different environments, from cryptocurrency such as Bitcoin and Ethereum, to other infrastructures and application domains such as the Internet-of-Things, Supply Chain, and Digital Health.
This course is to discuss the fundamentals of Blockchain technology, providing an overview of essential concepts to establish the foundation that is necessary for designing and developing blockchain applications. Beginning with an understanding of the technology and the philosophy of decentralization behind blockchain, the course progresses into technical protocols and architectures. We will study the decentralized peer-to-peer network, the immutable distributed ledger, and the security model that constitutes a blockchain. You will be able to comprehend basic components of a blockchain (transactions, blocks, nodes, etc.), its underlying algorithms, and cryptography (consensus, hashing, digital signatures, etc.).
After covering the fundamentals, we will review some of the enterprise implementations and practical uses of blockchain. We will explore several blockchain applications, focusing on current and potential uses beyond cryptocurrency. With that knowledge, you will be better prepared to critically analyze state-of-the-art blockchain protocols and the underlying technologies on which they operate.
CIS 6930: Human-Centered AI Interfaces for Sport and Wellness
Instructor: Kristy Boyer
Expected background: Statistics or data science course expected. Completion of a research methods course or experience with human subjects research will also be helpful but is not required.
Description: This seminar style course with a capstone project of the student’s choosing will examine recent advances in human-computer interaction for sport; AI for athletics; personal informatics for sport and wellness; and other emerging topics. Course meetings will focus on current literature including diverse publication venues and becoming familiar with the funding landscape. Graduate students in human-centered computing, computer science, health & human performance, and related disciplines are welcome. Course project will be student’s choice of literature review, empirical research study (with new or existing data), or new software design/development.
CIS 6930: Machine Learning and Genomics
Instructor: Kiley Graim
Expected background: None
Description: This course surveys the landscape of machine learning (ML) and artificial intelligence (AI) algorithms in genomics. The class uses a seminar format with in-class discussions of weekly readings covering the foundational research papers that introduced these algorithms in genomics. By the end of the course, students will have the ability to analyze primary computational biology literature, identify and formulate an AI/ML approach to solve a biological question, and understand foundational concepts and problems in computational biology, including but not limited to 3D protein structure modeling, pharmacogenomic design, precision medicine modeling, sequence and expression analysis, genotype-to-phenotype relationships, and regulatory network inference.
CIS6930 Data Engineering (grad only)
Instructor: Christian Grant
Expected background: None
Description: Data are the fundamental units in Artificial Intelligence (AI) and Machine Learning (ML) systems. Effectively harnessing this data is the responsibility of software engineers and data scientists. In this course, we will survey the landscape of AI/ML systems to understand how data flows through the systems. We will look at the engineer’s responsibilities for developing performant systems ethically and responsibly. Students will learn how to design, build, and evaluate data pipelines. We will cover the theoretical underpinnings of fairness and bias throughout data systems. Students will produce a comprehensive project using state-of-the-art systems that integrate best practices.
CIS4930/CIS6930 Internet Storage Systems (co-taught undergrad and grad sections)
Instructor: Jonathan Kavalan
Expected background: None
Description: Covers the fundamentals of using the Internet as the global storage systems for all data types. Understand the integrated design issues for Internet storage systems via the bottom-up approach; Survey of recent advances in data centers, centralized/distributed storage/file systems.
CIS6930 Generating Expressiveness
Instructor: Eakta Jain
Expected background: None
Description: Humans increasingly interact with physical and virtual agents in all areas of life and work. This course will expose students to foundational concepts and current state of the art in the multidisciplinary pursuit of generating expressive agents. Students will get an overview of the field and experience first-hand the challenges involved through project activities.
CIS6930: AI and Consciousness
Instructor: Anand Rangarajan
Expected background: None
Description: The problem of experience has become especially acute in the age of AI. The course will begin with the setup of the background to the hard problem of experience and why it resists being accommodated in the natural order. Since the problem of experience requires an in depth examination of intentionality and qualia, these concepts are unpacked next with a clear focus on representation, functionalism and complex systems. The debates between representation and emergence are then highlighted leading to the battle between abstract AI representations and concrete neuronal mechanisms underpinning intentionality and qualia. Subsequently, notions of embodiment and enaction are investigated since they offer a reconciliation between representation and emergence. After this detailed setup of the background issues, the hard problem of consciousness is then formulated with all forms of functionalism and emergence found wanting. Alternatives such as naturalistic dualism, panpsychism (and cosmopsychism), dual aspect theory and recent forms of idealism are discussed. In particular, quantum approaches to the hard problem are presented with the issue of ignorance regarding the true physical now front and center. The course concludes with brief expositions of Russellian and neutral monism and prospects for the future.
CIS 6930: Natural Language Processing Applications (3-6pm Tues) – cross-listed with CAI 6307 above.
Instructor: Bonnie J. Dorr
Expected background: Prerequisites: Proficiency in programming (Python recommended) & familiarity with introductory machine learning or artificial intelligence is a plus.
Description: Students will learn about Natural Language Processing applications, building upon on foundational concepts presented in the course including, but not limited to linguistic representations, parts of speech, parsing, semantic roles, n-gram models, sequence labeling, vector semantics, and language modeling. This course is ideal for satisfying 3 of the required “Special Topics” credits for the CISE Master’s program.
Students will be introduced to foundational concepts and their application to real-world problems, including an exploration of both symbolic and non-symbolic techniques to natural language problems, as well as techniques for evaluating the performance of natural language applications.
While cross-listed with CAI 6307, the course does not include a team-based research project. Instead, students will follow a research project in one of three student-selected application areas throughout the semester, focusing on two project presentations for that project.
Students will be assessed through: (1) quizzes and exams on their knowledge and application of NLP to real-world problems; (2) two exams tailored to the project they have chosen to follow, to demonstrate their understanding of the application approach, capabilities, results, and evaluation metrics.
CIS 4930: Special Topics: Internet Programming
Instructor: Albert Ritzhaupt
Expected background: Completion of COP 3503 with a passing grade or instructor permission.
Description: Course topics focus on software development and problem-solving applied to real-world problems with solutions designed and implemented in various languages. Topics include operating system concepts (e.g., Linux), mark-up languages (e.g., HTML), style sheets (e.g., CSS), client-side scripting (e.g., JavaScript), software libraries (e.g., jQuery), server side-scripting (e.g., PHP), relational databases (e.g., MySQL), web services and application programmer interfaces (e.g., JSON), software diagrams (e.g., ERDs), agile development principles (e.g., SCRUM), and other related technologies (e.g., regular expressions) used in full-stack web development. Prior programming experience is assumed and required. Students will extend course topics via programming assignments, concept quizzes, and a culminating web application group project extending across the duration of the semester.
CIS6930: Advanced User Experience Design (Advanced UXD)
Instructor: Lisa Anthony
Expected background: CEN 5728 User Experience Design
Description:User experience design (UXD) techniques used in industry or advanced research often need to be adapted to the context, in consideration of time, money, resources, and emerging interaction technologies. This course will build on the foundation taught in CEN 5278 User Experience Design. Graduate students will engage in a deep, iterative design cycle on a project of their choice, relevant to their research and/or industry goals. During class sessions, we will focus on student-driven discussions of how UXD methods have been updated and adapted in real use cases; and in-class teamwork on the semester project.