Special Topics

Special Topics courses cover some of our most innovative and promising research directions.  They are often prototypes of new courses that we are developing.

Special Topics course content changes every semester, so each semester's offerings are unique.

CSE 410 Computer Security (Lecture)
Section: BLAN
Instructor: Marina Blanton
Description: The objectives of this course consist of developing a solid understanding of fundamental principles of the security field and building knowledge of tools and mechanisms to safeguard a wide range of software and computing systems. A tentative list of the covered topics is: - cryptographic background and tools - access control - authentication - software security, malware - Internet security protocols and standards (SSL/TLS, IPsec, secure email) - intrusion detection and intrusion prevention systems (firewalls) - database security - privacy - identity management - security management and risk assessment - legal and ethical aspects (cybercrime, intellectual property)
Notes: This is an undergraduate offering of the computer security course previously offered only at the graduate level as CSE 565. While it covers similar topics to those studied in CSE 365 (undergraduate computer security), it goes into more technical depth and is expected to be more challenging. Having taken CSE 365 would make learning the material easier, but this is not a requirement.
Prereqs: Complexity and theory of computing background (CSE 396 or 331), operating systems background (CSE 220 or 421 or 365), and networking background (CSE 365 or 489 or 312).
Coreqs: CSE 365 can be taken at the same time to satisfy the prerequisite requirements.
Instruction Mode: Remote class, in person exam
Class #: 23792
Dates: 08/31/2020 - 12/11/2020
Days, Time: TR, 9:35AM-10:50AM
Location: Remote
Credit Hours: 3.00
Enrollment: 2/30 (0/30 seats reserved: force registration only)
Links: Registration: CSE 410LEC registration number 23792 calendar icon | Course Catalog: CSE 410LEC orange catalog icon
CSE 410 Introduction to Deep Learning (Lecture)
Section: CHEN
Instructor: Changyou Chen
Description: Recent years have witnessed significant success of deep learning techniques in machine learning, obtaining state-of-the-art results on various real-world tasks. In this course, we will go over deep-learning techniques from a basic level perspective. We will introduce related concepts and basic algorithms used in modern deep learning. Specifically, we will learn how to build deep learning models with feedforward neural networks, convolutional neural networks and recurrent neural networks, as well as algorithms for training deep neural network.
Notes: Machine learning, deep Learning
Prereqs: CSE 115, MTH141, MTH142, MTH309, EAS305 or MTH411 or STA301
Instruction Mode: Remote: real time and recorded
Class #: 23790
Dates: 08/31/2020 - 12/11/2020
Days, Time: TR, 2:20PM-3:35PM
Location: Remote
Credit Hours: 3.00
Enrollment: 10/30 (0/30 seats reserved: force registration only)
Links: Registration: CSE 410LEC registration number 23790 calendar icon | Course Catalog: CSE 410LEC orange catalog icon
CSE 410 Object Oriented Analysis, Design, and Implementation (Lecture)
Section: JAYA
Instructor: Bharat Jayaraman
Description: This course will cover the systematic design of object-oriented programs, including software requirements, high-level design in the Unified Modeling Language (UML), code development in a modern object-oriented language. The course will also cover use-case driven design, object-oriented design patterns, code contracts, distributed objects, interoperability, and static- and dynamic-typed languages.
Notes: This is the UG version of CSE 522.
Prereqs: CSE 250
URL: https://catalog.buffalo.edu/courses/index.php?abbr=CSE&num=522
Instruction Mode: Remote class, in person exam
Class #: 23793
Dates: 08/31/2020 - 12/11/2020
Days, Time: TR, 5:30PM-6:45PM
Location: Remote
Credit Hours: 3.00
Enrollment: 8/25 (0/25 seats reserved: force registration only)
Links: Registration: CSE 410LEC registration number 23793 calendar icon | Course Catalog: CSE 410LEC orange catalog icon
CSE 410 Quantitative equity portfolio management (Lecture)
Section: KEAN
Instructor: Kevin Keane
Description: This course will prepare students to research and implement quantitative equity strategies. This course will provide a self-contained overview, empirical examination, and detailed mathematical treatment of various topics that serve as the foundation of quantitative equity portfolio management. The course presents advanced techniques and applications in return forecasting models, risk management, portfolio construction, and portfolio implementation. Students will construct alpha models (stock performance forecasts), risk models, transaction cost models, and combine these inputs as a mathematical programming problem. The output from the optimization step will then be back tested and analyzed to evaluate strategy results. Reproducible research methods will be emphasized. Student work will be version controlled and shared with the instructor during the semester. Students may use the programming language of their choice (python, R, java, ...). Grading: class participation 10%; homework / mini-projects 60%; final project 30%.
Prereqs: Calculus, matrix algebra, probability, statistics, and computer programming.
Instruction Mode: Remote: real time and recorded
Class #: 25513
Dates: 08/31/2020 - 12/11/2020
Days, Time: TR, 3:55PM-5:10PM
Location: Remote
Credit Hours: 3.00
Enrollment: 5/15 (0/15 seats reserved: force registration only)
Links: Registration: CSE 410LEC registration number 25513 calendar icon | Course Catalog: CSE 410LEC orange catalog icon
CSE 410 Trustworthy and Explainable AI (Lecture)
Section: SREY
Instructor: Sreyasee Das Bhattacharjee
Description: In this course, we will discuss adversarial learning, analyze explainability as well as the security vulnerability and privacy related issues of different machine learning(ML)/Artificial Intelligence(AI) models, popularly used by the research community. While AI is growingly being employed as an automated decision making tool in several usecase settings like business, education, healthcare, law enforcement, etc., before adopting any such system, it is important for the end users to have a clear understanding of the questions like ‘why the system works?’ than treating it as an omnipotent BlackBox without having any explanation on its trustworthiness. We will review several state-of-the-art research papers to learn about the recent advances in this emerging domain of Trustworthy and Explainable AI, discuss several representative explainable models, learn about different categories of attacks along with a set of certified defenses introduced to evaluate robustness, and finally explore the connections between explainability and trustworthiness in terms of its applications in several domain specific problem settings.
Prereqs: CSE 116 and MTH 142 and MTH 309 and (CSE 469 or CSE 455 or CSE 474)
URL: https://cse.buffalo.edu/~sreyasee/CSE4510/
Instruction Mode: HyFlex
Class #: 24772
Dates: 08/31/2020 - 12/11/2020
Days, Time: MWF, 11:30AM-12:20PM
Location: Davis 101, North Campus
Credit Hours: 3.00
Enrollment: 0/15 (0/15 seats reserved: force registration only)
Links: Registration: CSE 410LEC registration number 24772 calendar icon | Course Catalog: CSE 410LEC orange catalog icon
CSE 410 Reinforcement Learning (Lecture)
Section: VERE
Instructor: Alina Vereshchaka
Description: This course is intended for students interested in artificial intelligence. Reinforcement learning is an area of machine learning where an agent learns how to behave in an environment by performing actions and assessing the results. Reinforcement learning is how Google DeepMind created the AlphaGo system that beat a high-ranking Go player and how AlphaStar become the first artificially intelligent system to defeat a top professional player in StarCraft II. We will study the fundamentals and practical applications of reinforcement learning and will cover the latest methods used to create agents that can solve a variety of complex tasks, with applications ranging from gaming to finance to robotics. The course is comprised of assignments, short weekly quizzes, a final project and a final exam.
Notes: Reinforcement Learning
Prereqs: CSE 574 or CSE 555 or CSE 573 is recommended to be either completed or taken during the same semester
Instruction Mode: Remote class, in person exam
Class #: 23496
Dates: 08/31/2020 - 12/11/2020
Days, Time: TR, 3:55PM-5:10PM
Location: Remote
Credit Hours: 3.00
Enrollment: 4/10 (0/10 seats reserved: force registration only)
Links: Registration: CSE 410LEC registration number 23496 calendar icon | Course Catalog: CSE 410LEC orange catalog icon