Updated: February 19th, 2025
CSE 190 - Topics in Computer Science and Engineering
CSE 190 is a topics of special interest in Computer Science and Engineering course. Topics may vary from quarter to quarter.
Prerequisites also vary per course/per instructor. Department approval required.
Units: 4
CSE 190 is typically offered every quarter as staffing allows.
CSE 190 may be repeated for credit a maximum of 3 times (maximum of 12 units; assuming courses taken for a different topic)
A maximum of one CSE 190 may be enrolled/waitlisted per quarter
Note: For the Fall 2023 Computer Science (CS26) curriculum, all CSE 190 courses will be labeled with a corresponding "Tag(s)" (Systems, Theory/Abstraction, and/or Applications of Computing). CSE 190 offerings before Fall 2023 are untagged but may be used as an Open CSE Elective for Computer Science majors who have changed to the FA23 CS26 curriculum.
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Spring 2025
CSE 190 A00: Introduction to Quantum Compu
Prerequisite: Required: Math 18 or Math 20F or Math 31AH. Recommended: All previous math experience will be very helpful, especially discrete math (Math 15A, CSE 20), probability (Math 11, CSE 21), and complex numbers. No prerequisite knowledge of physics or quantum computation is required. Class is unofficially co-scheduled with MATH 152. Students will not receive credit for both classes.
CS Curriculum Tag(s): Theory/Abstraction
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: This is an advanced undergraduate course focusing on the mathematical theory of quantum computers. The course will start with a general introduction to quantu
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CSE 190 B00: Working with Large Code Bases with Gerald Soosai Raj
Prerequisite: CSE 29 (or CSE 15L), Recommended Prerequisite: CSE110 (or co-requisite)
CS Curriculum Tag(s): Systems
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: This course introduces students to understand and work with large codebases. Students will learn how to fix bugs and modify/add features in existing unfamiliar code. Students will learn processes, techniques, and tools for working with large codebases. Students will work in small teams on a large open-source codebase throughout the course. By the end of the course, students will develop practical skills and confidence to collaborate on complex software projects in real-world settings.This course is recommended for students *without* internship experience, although anyone is welcome to register.
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CSE 190 C00: Introduction to Deep Reinforcement Learning with Prithviraj Ammanabrolu
Prerequisite: (CSE 12 or DSC 40B) and (CSE 15L or CSE 29 or DSC 80) and (COGS 118D or CSE 103 or ECE 109 or ECON 120A or MAE 108 or MATH 180A or MATH 180B or MATH 181A or MATH 183 or MATH 186) and (CSE 100 or CSE 100R)
CS Curriculum Tag(s): Applications of computing
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: This course will build the concepts required to understand modern advances in reinforcement learning and its intersection with deep learning. It will start with search and reasoning techniques as well as provide an overview of the deep learning concepts like gradient descent up to LLMs. It will end in the intersection of the two talking about modern advancements like Deep Q Networks and RL used with LLMs. This will be an assignment focused class and students will be expected to code many of the concepts learned in class using Python.
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CSE 190 D00: Introduction Blockchain Security with Deian Stefan
Prerequisite: CSE 110
CS Curriculum Tag(s): Systems ; Applications of Computing
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: This course focuses on the security of blockchain technologies. Students will be introduced to many parts of the blockchain infrastructure — from the design and implementation of consensus protocols, to the programming layers above them, and the DeFi applications handling billions of dollars — and explore the different ways these systems can—and have—failed under adversarial settings. We will study attacks against real systems, revisit the security and assumptions of widely deployed protocols and applications, and analyze the impact of attacks, scams, and thefts.
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Winter 2025
CSE 190 A00: Algorithms and Systems for Biomolecular Big Data with Nuno Bandeira
Prerequisite: No course prerequisites.
CS Curriculum Tag(s): Applications of Computing
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: Computational analysis of massive volumes of data holds the potential to transform society. However, the computational translation of data into knowledge requires more than just data analysis algorithms – it also requires proper matching of data to knowledge for interpretation of the data, testing pre-existing knowledge and detecting new discoveries. We will study these concepts in the context of the longest-running (and arguably most important) human quest for knowledge of vital importance: the use of biomolecular big data to study human health and disease. With the sequencing of the genome and subsequent identification of the list of parts (i.e., genes and their protein products), there is renewed emphasis on understanding the many roles of the protein gene products using automated high-throughput approaches. The last few years have thus seen tremendous improvement in the quality and quantity of available data, as well as the realization that advanced algorithms and big data crowdsourced platforms are critical to the success of this technology. This course will cover the statistical, algorithmic and systems foundations of the various computational challenges arising from the need to match such data to related knowledge bases, with a special emphasis on investigations of cancer and infectious diseases (including the SARS-CoV-2/COVID19 pandemic). In addition, we will also cover data science aspects of how these algorithms are integrated with global big data repositories and how these support communities of experts in distributed collaborative annotation of biomolecular ‘living data’.
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CSE 190 B00: Wireless Embedded Systems with Aaron Schulman (Shalev)
Prerequisite: CSE 29 or CSE 30 (CSE 120 recommended)
CS Curriculum Tag(s): Systems
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: Wireless embedded systems bridge our physical world with powerful digital control systems and cloud data analytics. Applications range from medical devices such as Bluetooth-enabled blood glucose meters, to payment systems such as Near-Field Communication-based credit cards. In this class, students will learn about how an embedded system works from the ground up. The lectures will focus on the key enabling components of embedded systems, including: Clocks, GPIO, Interrupts, Busses, Power management, ADC/DAC, DMA, Storage, and Wireless communication. The goal of the class is to familiarize the students with these components so that they feel comfortable working on a team that is building a device that incorporates a wireless embedded system Prerequisites: CSE 29 or CSE 30 (CSE 120 recommended)
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CSE 190 C00: Data Center Systems with Amy Ousterhout
Prerequisite: CSE 120
CS Curriculum Tag(s): Systems
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: Data centers form the backbone of large-scale applications today, powering everything from popular online platforms such as Zoom, TikTok, and ChatGPT to critical enterprise systems in healthcare and other industries. In this course, students will learn how next-generation data centers work. We will explore the characteristics of data center applications and data center hardware (servers, networks, accelerators) and we will learn how to provide high performance, isolation, reliability, and energy efficiency for data center applications. We will also discuss the security, economic, and sustainability challenges facing data center operators. Students will gain hands-on experience with these concepts by working in small groups to implement a cloud application, deploy it in the cloud, and analyze its performance.
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CSE 190 D00: Fairness, bias, and transparency in Machine Learning with Julian McAuley
Prerequisite: No course prerequisites. Class is unofficially co-scheduled with CSE 291 Section J00. Students will not receive credit for both classes.
CS Curriculum Tag(s): Applications of Computing
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: This course is devoted to fairness, bias, and transparency in machine learning. After taking this course, students will be able to understand the main sources of bias and unfairness in machine learning systems, and deploy strategies to mitigate these biases. Students will also understand the related notions of accountability and transparency in Machine Learning, allowing for the development of systems that are more trustworthy.
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Fall 2024
CSE 190 A00: Human-Centered AI with Kristen Vaccaro
Prerequisite: No course prerequisites.
CS Curriculum Tag(s): Applications of Computing
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: This course provides an introduction to harnessing the power of AI so that it benefits people and communities. Topics will include: agency and initiative, fairness and bias, transparency and explainability, confidence and errors, and privacy, ethics, and trust. Students will build a number of interactive technologies powered by AI, gain practical experience with what makes them more or less usable, and learn to evaluate their impact on individuals and communities. Students will learn to think critically (but also optimistically) about what AI systems can do and how they should be integrated into society.
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CSE 190 B00: Programmers are People Too with Michael Coblenz
Prerequisite: CSE 110 or CSE 130. Class is unofficially co-scheduled with CSE 291 Section B00. Students will not receive credit for both classes.
CS Curriculum Tag(s): Applications of Computing
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: Programmers of all kinds express their ideas using programming languages. Unfortunately, languages can be hard to use correctly, resulting in lengthy development times and buggy software. How can these languages be designed to make programmers as effective as possible?
In this course, we will learn research methods for analyzing and improving the usability of programming languages. Students will apply these techniques to languages of current and historical interest, and in the process, expand their knowledge of different ways to design languages. This course is intended as preparation for conducting independent research on the usability of programming languages. The first part of the course will emphasize research methods from human-computer interaction research, with examples drawn from programming languages and development environments. The second part of the course will focus on reading research papers that describe key results from the field. The course will include homework assignments as well as a group research project.