{"id":20459,"date":"2025-07-11T17:32:28","date_gmt":"2025-07-11T21:32:28","guid":{"rendered":"https:\/\/www.bu.edu\/cs\/?page_id=20459"},"modified":"2026-04-06T10:11:01","modified_gmt":"2026-04-06T14:11:01","slug":"topics-courses-archive","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/cs\/undergraduate\/courses\/topics\/topics-courses-archive\/","title":{"rendered":"Topics Courses Archive"},"content":{"rendered":"

Topics Courses Archive<\/h3>\n

Fall 2026 Course Descriptions<\/h4>

\nCS391 R1: Computer and Memory Architectures (must also enroll in R2 or R3 discussion section)<\/span><\/strong><\/p>\n

Prerequisites:<\/strong> CS210 or equivalent<\/span><\/p>\n

Content:<\/b> Want to build a computer processor? This course is a hands-on approach to the design of modern computing and memory architectures, with an emphasis on implementation and quantitative evaluation. Topics include digital logic and arithmetic-logic units (ALU); in-order pipelined central processing unit (CPU) microarchitecture; multi-level cache hierarchies and cache coherence protocols; and multicore processor systems. Additional topics may include advanced memory technologies, bus protocols, and hardware accelerators. The practical laboratory component of the course includes development of modules using a modern hardware description language (HDL), and assignments may include implementing designs on an FPGA.<\/span><\/p>\n

Course Site: <\/b>https:\/\/sites.google.com\/bu.edu\/cs391r1-fall2025<\/span><\/a><\/p>\n

Instructor:<\/strong> Sabrina Neuman<\/span><\/p>\n

 <\/p>\n

CS391 A1: Web Application Development (must also enroll in A2, A3, A4, or A5 discussion section)<\/strong><\/span><\/p>\n

Prerequisites:<\/strong> CS111, CS112, CS210<\/span><\/p>\n

Content:<\/b> Web Application Development is a comprehensive course empowering students to build dynamic web apps. Through hands-on projects, they learn essential code management with Git\/GitHub, frontend languages like HTML\/CSS, and interactive app development with JavaScript. React is introduced to simplify UI creation and promote code reusability. Students will explore industry-standard tools like Next.js, Vercel, and MongoDB.\u00a0<\/span><\/p>\n

Instructor:<\/strong> Prof. Taymaz Davoodi<\/span><\/p>\n

CS 103 A1 : Introduction to Internet Technologies and Web Programming\u00a0<\/strong><\/span><\/h3>\n

Introduction to the basic architecture and protocols underlying the operation of the Internet with an emphasis on web design, web application programming, and algorithmic thinking. General familiarity with the Internet is assumed. Carries MCS divisional credit in CAS. Effective Fall 2022, this course fulfills a single unit in each of the following BU Hub areas: Digital\/Multimedia Expression, Quantitative Reasoning II, Creativity\/Innovation.<\/span><\/p>\n

 <\/p>\n

CS392 X1: A faster-paced version of CS112 (Introduction to CS II)<\/strong><\/span><\/p>\n

Prerequisites:<\/strong> This course is designed for students who already have a basic level of proficiency in Java. In terms of course requirement, it is equivalent to CS112.\u00a0<\/span><\/p>\n

Content: This course largely follows the following book (available on-line for free):<\/span><\/p>\n

Algorithms by R. Sedgewick and K. Wayne, 4th edition, 2011.<\/span><\/p>\n

It starts by quickly revisiting and then building upon basic programming concepts in Java. The main focus of the course is on the design, analysis and implementation of fundamental data structures used throughout computer science. These include linked lists, stacks, queues, trees, hash tables, graphs, as well as specialized methods for searching and sorting. All of our implementations will be written in the object-oriented programming language Java, making use of advanced programming features including abstract classes, generics, higher-order methods, and lazy evaluation.<\/span><\/p>\n

Instructor:<\/strong> Hongwei Xi<\/span><\/p>\n

CS 599 M1: Deep Visual Generative Models<\/span><\/strong><\/p>\n

Graduate Prerequisites:<\/strong> This course is designed for CS Ph.D., Master, and senior undergraduate students who are interested in deep generative models and their application in computer vision. In order to get the most out of this course, you will need a solid understanding of machine learning, deep learning, and computer vision.<\/span><\/p>\n

Undergraduate Prerequisites<\/strong>: CS 542 (or 541), CS 585, and CS 523, or permission of instructor.<\/span><\/p>\n

Content: This graduate-level seminar course delves into deep generative models, with a primary focus on their applications in computer vision (e.g., image and video generation). We will explore variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, and autoregressive models, and discuss how these frameworks are applied to diverse visual data formats. We will also cover the foundational models built upon these techniques, including but not limited to the foundation models\u2019 generalization, efficiency, and benchmarking.\u00a0<\/span><\/p>\n

This seminar course is for graduate students ready to explore the latest research frontiers and actively conduct research in this domain.<\/span><\/p>\n

Instructor:<\/strong> Boqing Gong<\/span><\/p>\n

CS501 E2: Mobile Application Development<\/strong><\/span><\/p>\n

Prerequisites:<\/strong> No prior experience in mobile application development is required. However, students must have a solid understanding of object-oriented programming and database development, equivalent to the material covered in CS 112 and CS 460. Required skills include fluency with object-oriented design and implementation, control structures, loops, arrays, XML, and basic database concepts. GUI and event-driven programming concepts will be introduced as part of the course.<\/span><\/p>\n

Content: This graduate-level, hands-on practicum explores modern Android mobile application development using Kotlin, Jetpack Compose, and the Android SDK. Students will follow agile development practices and begin the course by building a series of small mobile apps to gain familiarity with core Android concepts. Midway through the semester, students will form small teams (2\u20133 members) and collaborate on a substantial final project.<\/span><\/p>\n

The course emphasizes a modern, industry-aligned development workflow and integrates the use of AI-assisted development tools (e.g., Android Studio\u2019s AI features) for code generation, documentation, and testing\u2014requiring students to critically evaluate and reflect on AI contributions. This is a coding-intensive, project-based course with high expectations for collaboration, research, and independent learning. Students will be assessed through individual assignments, team research reports, two interim project reviews, and a final project with presentation.<\/span><\/p>\n

Instructor:<\/strong> Ron Czik<\/span><\/p>\n

<\/h3>\n

CS 598 A1 : Agentic AI for Everything<\/span><\/strong><\/p>\n

Prerequisites:<\/strong> Students must have completed any one of CS 440, CS 505, CS 523, CS 541 or CS 542 or with a grade of B+ or higher, or obtain permission from the instructor. Strong programming skills required. Familiarity with LLM API integration recommended.<\/span><\/p>\n

Content: We are rapidly transitioning from the era of static chatbots to the age of Agentic AI: autonomous systems capable of reasoning, planning, and executing complex workflows without constant human oversight. While standard LLMs can answer questions, Agentic AI can actually take action, coordinating tools, code, and information to complete real tasks end to end, basically do everything in the digital realm!<\/span><\/p>\n

This course is a practical, hands-on deep dive into building agentic systems that automate daily life and professional workflows. Have you ever wanted to build a digital assistant that can book travel, write and run code, or manage email autonomously? Students will master core agent fundamentals, including reasoning loops (ReAct), memory and context management, tool use and function calling. The course also examines practical limitations and failure modes such as hallucination propagation and safety constraints, alongside active research directions like multi-agent systems, RLVR, and agentic skill acquisition. During the semester, students are expected to fully implement their own personal agent in the style of modern agents such as OpenClaw or Claude Code, including functionalities for tool use, memory management and iterative execution.<\/span><\/p>\n

Additional details can be found:<\/span><\/p>\n

https:\/\/docs.google.com\/document\/d\/1RpXJF_8R6H8NGkWSjn1VyNWGFo70U9limEzPWGP3NAI\/edit?usp=sharing<\/span><\/a><\/p>\n

Instructor:<\/strong> Chang Xiao<\/span><\/p>\n

CS 599 B1: Advanced Natural Language Processing<\/strong><\/span><\/p>\n

Prerequisites<\/strong>: at least one of CS365, CS440, CS505, CS523, CS541, CS542, or CS585; or permission of instructor.<\/span><\/p>\n


\n<\/span>Natural language processing (NLP) systems are now ubiquitous: ChatGPT, recommendation systems, machine translation systems, among others are used daily by many people. Advanced natural language processing is a course aimed at students who are interested in learning about the current state-of-the-art techniques, and doing novel research in the field.<\/span><\/p>\n

The course will focus primarily on deep learning methods for NLP. This includes the Transformer and its alternatives (e.g., state-space models), post-training and test-time compute scaling, model compression, long-context reasoning tasks, and representation steering\/engineering, among other topics. Students will read research papers and gain hands-on experience implementing and applying current methods. The course culminates in an original research project, where students will design and run experiments aimed at generating novel insights or addressing a real-world problem.<\/span><\/p>\n

Instructor:<\/strong> Aaron Mueller<\/span><\/p>\n

<\/h3>\n

CS 599 G1 : The Geometry of Polynomials in Algorithms<\/strong><\/span><\/p>\n

Prerequisites<\/strong>: Strong undergraduate background in probability, analysis, and algorithms. At least one of CS530 or CS537 is recommended .<\/span><\/p>\n

Description: A classic technique in mathematics is to encode a combinatorial object in the coefficients of a polynomial. For example, given a discrete probability distribution which is {1} with probability 1\/2 and {1,2} otherwise, we can associate the polynomial (1\/2)xy + (1\/2)x. This encoding allows us to study the polynomial’s behavior as a function, its zeros, and how these relate to its coefficients, often revealing structure about our object we might be unable to find otherwise. A major focus of the course will be on real stable and hyperbolic polynomials, generalizations of real rooted polynomials to the multivariate case. After developing a background in this area, we will see how tools from this field relate to problems in algorithms and TCS.<\/span><\/p>\n

Instructor:<\/strong> Nathan Klein<\/span><\/p>\n

<\/h3>\n

CS 501 S1 : Agile Mobile Application Development<\/strong><\/span><\/p>\n

Great App Developers are creative, collaborative, thoughtful, understand good design principles and care about their user\u2019s experience. In this highly collaborative course, students will model agile industry design practices to implement rich, cross-platform, data driven applications.\u00a0<\/span><\/p>\n

Students are expected to be able to leverage their programming skills and pivot between multiple languages. Use of AI is welcome, but only as a tool to facilitate development, not a replacement to critical thinking or understanding of important design principles. Students will apply programming skills across multiple languages and frameworks, beginning with .NET and moving into other technology stacks. The course starts with individual exercises on UI components and event-based programming, then progresses to team projects covering advanced topics such as scalable design, asynchronous programming, exception handling, RESTful 3rd party API integration, authentication, multithreading, dependency injection, and advanced features like delegates, lambda expressions, and Language Integrated Queries (LINQ).\u00a0<\/span><\/p>\n

In the second half, students conduct independent research to implement a Final APP, which serves as a portfolio piece often used in interviews to secure internships or jobs.\u00a0<\/span><\/p>\n

Prior app development experience is not required, but object-oriented programming knowledge (along with the ability to pivot from one language to another), creativity, and teamwork are essential.\u00a0<\/span><\/p>\n

Instructor:<\/strong> Shereif El-Sheikh<\/span><\/p>\n

<\/h3>\n

CS 599 A1:\u00a0 Programming Massively Parallel Multiprocessors and Heterogeneous Systems<\/strong><\/span><\/p>\n

Prerequisites:<\/strong> This course is designed for CS PhD students interested in exploring the programming and optimization of GPU software. \u00a0 You must have a basic understanding of the C\/C++ programming language (especially its use of pointers) and an undergraduate level knowledge of computer architecture.<\/span><\/p>\n

Content:<\/b> This graduate course aims to provide hands-on experience in developing applications software for graphics processors with massively parallel computing resources.\u00a0 The target audiences of the course are those who want to develop exciting applications for these processors, those who want to develop programming tools and future implementations for these processors, and those who want to understand how to program these devices at the level of C\/C++.\u00a0 The initial part <\/span>of the course focuses on popular programming interfaces for these processors.\u00a0 The course continues with a closer view of the internal organization of graphics processors and how it impacts performance.\u00a0 Finally, implementations of applications and algorithms on these processors will be discussed.\u00a0 Students will be encouraged to use a problem from their research as the topic of their course project.\u00a0\u00a0<\/span><\/p>\n

Instructor: Jonathan Appavoo<\/span><\/p>\n

<\/div>\n<\/div>\n\n

\n

Spring 2026 Course Descriptions<\/h4>
<\/p>\n

CS391 A1: Web Application Development (must also enroll in A2, A3, A4, or A5 discussion section)<\/u><\/b><\/p>\n

Prerequisites:<\/b> CS111, CS112, CS210<\/span><\/p>\n

Content:<\/b> Web Application Development is a comprehensive course empowering students to build dynamic web apps. Through hands-on projects, they learn essential code management with Git\/GitHub, frontend languages like HTML\/CSS, and interactive app development with JavaScript. React is introduced to simplify UI creation and promote code reusability. Students will explore industry-standard tools like Next.js, Vercel, and MongoDB.\u00a0<\/span><\/p>\n

Instructor:<\/b> Prof. Taymaz Davoodi<\/span><\/p>\n

\n

CS391 J1: Programming Massively Parallel Multiprocessors and Heterogeneous Systems \u2013 \u0192ankus<\/u><\/b><\/p>\n

Prerequisites<\/b>:\u00a0 Required, CS210. Additionally, it is recommended that you have taken CS350 or CS351.<\/span><\/p>\n

Description<\/b>: While the CPU’s familiar von Neumman architecture and programming model have dominated general-purpose computing, today’s systems are heterogeneous.\u00a0 Most incorporate Massively Parallel MultiProcessors, in the form of GPUs.\u00a0 The high-performance data parallel architecture and programming model of the GPU have proven critical in providing the raw computation power required for modern AI computation. Understanding the heterogeneous combination and how to exploit it effectively and practically is the focus of this class.<\/span><\/p>\n

The course covers “general purpose” — i.e., non-graphical — programming techniques for GPUs in a heterogeneous system.\u00a0 The course introduces NVIDIA’s parallel computing language, CUDA, including its programming model and syntax.\u00a0 We also discuss GPU architecture, high-performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.<\/span><\/p>\n

The focus of the course will be on performance.\u00a0 This is because, for general-purpose parallel programs, GPUs are often used as accelerators to speed up the programs so that they run faster than they would on a multicore CPU.\u00a0 Specifically, the programming assignments and your project will require you to evaluate the various micro-architectural features of GPUs experimentally.\u00a0 This course is not about getting applications run on a GPU using existing libraries and frameworks, but rather about gaining a fundamental understanding of how GPUs work and interact with CPUs concerning performance.<\/span><\/p>\n

Instructor: <\/b>Prof. Jonathan Appavoo<\/span><\/p>\n

\n

CS392 C1: Competitive Programming\u00a0<\/u><\/b><\/p>\n

Prerequisites<\/b>: CS112, CS131, prior knowledge in C\/C++ or Java<\/span><\/p>\n

Description<\/b>: This course covers basic algorithms necessary to compete in the ACM International Collegiate Programming Contest (ICPC) and similar contests. Active involvement in weekly contests is a mandatory component of the course. Highly recommended to anyone who wants to prepare for typical fundamental data structure\/algorithm part of a job interview at top IT companies. Topics covered include standard library classes and data structures, competitive programming contest strategies, string manipulation, divide and conquer, dynamic programming, graph algorithms, number theory, computational geometry, and combinatorics.<\/span><\/p>\n

Instructor<\/b>: Prof. Tiago Januario<\/span><\/p>\n

\n

CS392 X1: A faster-paced version of CS112 (Introduction to CS II)<\/u><\/b><\/p>\n

Prerequisites<\/b>: This course is designed for students who already have a basic level of proficiency in Java. In terms of course requirement, it is equivalent to CS112.\u00a0<\/span><\/p>\n

Content<\/b>: This course largely follows the following book (available on-line for free):<\/span><\/p>\n

Algorithms by R. Sedgewick and K. Wayne, 4th edition, 2011.<\/b><\/p>\n

It starts by quickly revisiting and then building upon basic programming concepts in Java. The main focus of the course is on the design, analysis and implementation of fundamental data structures used throughout computer science. These include linked lists, stacks, queues, trees, hash tables, graphs, as well as specialized methods for searching and sorting. All of our implementations will be written in the object-oriented programming language Java, making use of advanced programming features including abstract classes, generics, higher-order methods, and lazy evaluation.<\/span><\/p>\n

Instructor<\/b>: Hongwei Xi<\/span><\/p>\n

\n

CS501 E1: Mobile Application Development<\/u><\/b><\/p>\n

Prerequisites: <\/b>No prior experience in mobile application development is required. However, students must have a solid understanding of object-oriented programming and database development, equivalent to the material covered in CS 112 and CS 460. Required skills include fluency with object-oriented design and implementation, control structures, loops, arrays, XML, and basic database concepts. GUI and event-driven programming concepts will be introduced as part of the course.<\/span><\/p>\n

Content: <\/b>This graduate-level, hands-on practicum explores modern Android mobile application development using Kotlin, Jetpack Compose, and the Android SDK. Students will follow agile development practices and begin the course by building a series of small mobile apps to gain familiarity with core Android concepts. Midway through the semester, students will form small teams (2\u20133 members) and collaborate on a substantial final project.<\/span><\/p>\n

The course emphasizes a modern, industry-aligned development workflow and integrates the use of AI-assisted development tools (e.g., Android Studio\u2019s AI features) for code generation, documentation, and testing\u2014requiring students to critically evaluate and reflect on AI contributions. This is a coding-intensive, project-based course with high expectations for collaboration, research, and independent learning. Students will be assessed through individual assignments, team research reports, two interim project reviews, and a final project with presentation.<\/span><\/p>\n

Instructor:<\/b> Ron Czik<\/span><\/p>\n

\n

CS501 S1: Agile Application Development\u00a0<\/u><\/b><\/p>\n

Content<\/b>: Learn to build cross-platform, data-driven applications using industry-standard design principles. This team-based, hands-on course teaches students to design and develop rich applications with a strong focus on user experience, creativity, and collaboration. Students apply programming skills across multiple languages and frameworks, beginning with .NET.<\/span><\/p>\n

The course starts with individual exercises on UI components and event-based programming, then progresses to team projects covering advanced topics such as scalable design, asynchronous programming, exception handling, RESTful API integration, authentication, multithreading, dependency injection, and advanced C# features like delegates, lambda expressions, and LINQ.<\/span><\/p>\n

In the second half, students conduct independent research to implement a Final APP, which serves as a portfolio piece often used in interviews to secure internships or jobs. Prior app development experience isn\u2019t required, but object-oriented programming knowledge, creativity, and teamwork are essential.<\/span><\/p>\n

Instructor<\/b>: Shereif El-Sheikh<\/span><\/p>\n

\n

CS 598 G1: AI, Cybersecurity and Entrepreneurship\u00a0<\/u><\/b><\/p>\n

Content:<\/b> This course will explore technology entrepreneurship with a specific focus on AI and cybersecurity companies. The goal is to learn about the technology behind the latest cybersecurity and AI companies alongside the business considerations involved in building these companies from the ground up.\u00a0\u00a0<\/span><\/p>\n

Topics we cover include product design, business models, product-market fit, basic sales and marketing frameworks, startup financing, fundraising metrics and different stages in the startup life cycle (pre-Seed, Seed, Series A, Series B. . . ). \u00a0 We will also have a substantial number of guest lectures from local startup founders, entrepreneurs, investors and executives.<\/span><\/p>\n

Evaluation is based mostly on hands-on labs and course projects that involve analyzing the latest exciting cybersecurity and AI startups.\u00a0 Some of the examples of companies we discussed in depth last year include Tailscale, Twingate, Jumpcloud, Wiz, AppMap, and HYPR.\u00a0<\/span><\/p>\n

This course fulfills the CS Group D requirement for undergraduates.\u00a0 If you are a student in the minor in Entrepreneurship and Innovation, the professor will work with you to get this course included in your minor.\u00a0<\/span><\/p>\n

The course is taught by Prof. Sharon Goldberg, who was the co-founder and CEO of a cybersecurity startup, BastionZero, that was acquired by Cloudflare in May 2024. She is also an infrastructure cybersecurity researcher and has been a tenured professor in the CS department at BU since 2010.\u00a0<\/span><\/p>\n

Instructor: <\/b>Sharon Goldberg<\/span><\/p>\n

\n

CS598 P1: Multimodal Machine Learning (must also enroll in P2-P3 discussion section)<\/u><\/b><\/p>\n

Prerequisites: <\/b>CS542 (Machine Learning) or equivalent<\/span><\/p>\n

Many applications of artificial intelligence rely on reasoning about data from many different sources (e.g., images, video, language, sound, infrared, Lidar, etc).\u00a0 This course serves as an introduction to methods that aim at using the fusion of these data sources to accomplish downstream tasks.\u00a0 For example, automatically captioning an image requires reasoning about visual and text data, a robot searching for a person lost in a cave may rely on infrared and sound, and autonomous vehicles may use Lidar and video information.\u00a0 This class will explore machine learning and statistical techniques that aim to understand the relationship between modalities.\u00a0 Students will also learn about some of the common issues that arise when dealing with multiple modalities such as data scarcity,\u00a0 positive-unlabeled learning, structured prediction, and the challenges in evaluating these systems.<\/span><\/p>\n

Professor:<\/b> Bryan Plummer<\/span><\/p>\n

Course site: <\/b>See link on https:\/\/www.bryanplummer.com\/<\/span><\/p>\n

\n

CS 598 X1: Human-Computer Interaction and Human-AI Interaction<\/u><\/b><\/p>\n

Prerequisites:<\/b>\u00a0 Required, CS365 or CS440\u00a0<\/span><\/p>\n

Content: <\/span>This course examines two fields, HCI and AI, once described as \u201ctwo fields divided by a common focus.\u201d Historically, they often competed for intellectual and economic resources, but today their convergence is increasingly important. This course explores that convergence by combining classic HCI principles with the latest developments in HAI and AI agents.<\/span><\/p>\n

The first half of the course introduces the foundations of HCI: usability principles, interaction techniques, design heuristics, and methodologies for conducting user studies. Students will gain hands-on experience with both quantitative and qualitative research methods, learning how to rigorously evaluate systems from a human-centered perspective.<\/span><\/p>\n

The second half shifts toward human-centered AI and the rapidly evolving frontier of AI agents and large language models. Topics include prompt engineering, AI interface design, Agentic AI workflow and emerging methods such as simulating user studies with AI. Students will learn to analyze, design, and evaluate AI-powered systems through the lens of HCI, bridging the gap between theory and practice.<\/span><\/p>\n

Students will complete several assignments and a final project, gaining skills in both rigorous HCI methodology and frontier approaches to building and evaluating AI systems. This lecture-based course prepares students to design, analyze, and critically engage with modern AI-powered systems from a human-centered perspective.<\/span><\/p>\n

Instructor:<\/b> Chang Xiao\u00a0<\/span><\/p>\n

\n

CS 599 A1: Logic and Programming Languages<\/u><\/b><\/p>\n

Prerequisites: <\/b>Required: CS 131, CS 210. Recommended: CS 320<\/span><\/p>\n

Content:<\/b> What is truth?<\/b> In constructive logic, a proof isn\u2019t just a certificate that something is true \u2014 it\u2019s also an explicit construction or algorithm. This course introduces constructive (or intuitionistic) logic, a foundation for mathematics and computer science that emphasizes proofs as constructive evidence rather than abstract truth. Students will explore the principles that distinguish constructive logic from classical logic, including the rejection of the law of excluded middle and the requirement that proofs yield explicit witnesses.<\/span><\/p>\n

The course will cover:<\/span><\/p>\n

Textbook<\/b>: <\/span>https:\/\/cpbook.net\/details?cp=4<\/span><\/a>\u00a0<\/span><\/p>\n