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Summary

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Date Taken: Fall 2023
Instructor: Cynthia Rudin
Summary: This is an introductory overview course at an advanced level. Covers standard techniques, such as the perceptron algorithm, decision trees, random forests, boosting, support vector machines and reproducing kernel Hilbert spaces, regression, K-means, Gaussian mixture models and EM, neural networks, and multi-armed bandits. Covers introductory statistical learning theory.
Grade Received: A-

Date Taken: Fall 2023
Instructor: Bruce Maggs
Summary: Basic systems support for process-to-process communications across a computer network. The TCP/IP protocol suite and the Berkeley sockets application programs interface. The topics include congestion control, packet scheduling, routing, software defined networking, datacenter networks, network function virtualization, programmable switches, network measurement, remote direct memory access, residential networks, peer-to-peer networks, and content distribution networks.
Grade Received: B

Date Taken: Fall 2023
Instructor: Jian Pei
Summary:Data science is 'the science of planning for, acquisition, management, analysis of, and inference from data'. This course systematically covers the concepts, ideas, tools, and example applications of data science in an end-to-end manner. We emphasize data-driven thinking, data processing and analytics, and extracting actionable values from data. We focus on the interactions between data and applications, data modeling, and data processing, data analytics, and the essential algorithms and tools.
Grade Received: B+

Date Taken: Spring 2024
Instructor: Pardis Emami Naeini
Summary: This course will introduce several security and privacy topics that have strong human factors component. Some of the themes that we cover throughout this course include overview of User Research Methods and Ethics, Equity and Inclusivity in Security and Privacy, Challenges In Designing Usable Security and Privacy Tools, Security and Privacy Education and Awareness, and Human-Centered Security and Privacy in Emerging Technologies. This course includes weekly reading commentaries, a midterm exam, and a final group research project.
Grade Received: A

Date Taken: Spring 2024
Instructor: Dennis Quan
Summary: CS590 Advanced Web Development is a graduate-level course that delves into the theoretical foundations and practical applications of cutting-edge web technologies. Students will master advanced techniques in web development, including the use of Docker and Kubernetes for containerization and orchestration, MongoDB for database management, and OAuth for secure authentication. The curriculum also covers client-side and server-side programming, employing modern web frameworks to design responsive, scalable, and secure web applications. This course is designed to equip students with the comprehensive skills needed to excel in professional web development environments.
Grade Received: A-

Date Taken: Spring 2024
Instructor: Carlo Tomasi
Summary: Image formation and analysis; feature computation and tracking; image, object, and activity recognition and retrieval; 3D reconstruction from images.
Grade Received: A

Date Taken: Fall 2024
Instructor: Boyuan Chen
Summary: This is an advanced seminar course on AI-enabled robotics. The key question we will look at is how to build generalist robots that constantly learn, act and improve through natural interactions with the environment. We will study how robots perceive and model the complex world, make plans and decisions, and robustly adapt to various environmental conditions. Students will read, present and discuss the latest research on robot learning which involve areas in robot perception, manipulation, navigation, motion and task planning, robot and sensor design, and multi-robot systems. Throughout this course, students will also conduct a research-level project on robot learning topics.
Grade Received: A

Date Taken: Fall 2024
Instructor: Patrick Wang
Summary: Introduction to algorithmic and analytic methods specific to textual data. Subject areas covered are speech recognition, optical character recognition, text parsing, and document analysis. Analysis tools taught include sentiment analysis/topic models, auto-correct, auto-complete, and translation systems. Applications to brain-computer interface communication systems, intelligent personal assistants, and plagiarism detection systems.
Grade Received: A

Date Taken: Fall 2024
Instructor: Richard Telford
Summary: Explores mobile application development in the Apple Development Environment. Uses core software engineering pillars of Swift, Xcode, iOS & Xcode Cloud to learn how to create apps for Apple products. Focuses on iOS/iPhone, but Xcode also allows for exploration into the VisionPro, the Apple Watch, the iPad, and Apple TV. Real world context focused on common programming patterns for engineers in academia or business - standalone apps, apps connected to other systems, apps connected to the cloud. Covers software engineering fundamentals essential to understanding all aspects of app development. Each team will deliver a fully functioning app.
Grade Received: A
TravelLog Project

Date Taken: Spring 2025
Instructor: Noah Gift
Summary: This course is designed to give students a comprehensive view of cloud computing including Big Data and Machine Learning. A variety of learning resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure). This is a project-based course with extensive hands-on assignments.
Grade Received: Currently Taking