Updated: 3/18/2021
CSE 190 - Topics in Computer Science and Engineering
Units: 4
Course Description: Topics of special interest in Computer Science and Engineering. Topics may vary from quarter to quarter.
Prerequisites: Prerequisites vary per course per instructor. Department stamp required.
Offered: Every quarter as staffing allows
- May be repeated for credit max 3 times (maximum of 12 units; assuming courses taken for a different topic)
- A maximum of ONE CSE 190 may enrolled/waitlisted per quarter
Summer Session 2021
S121 and S221 CSE 190 A00: Machine Learning for Music and Audio with Shlomo Dubnov
Prerequisite(s): MATH 18 AND MATH 20B AND (CSE 103 or ECON 120A or MATH 183 or ECE 109 or MATH 180A or MATH 181A) or instructor approval. Programming ability in Python required. Musical skills are not required but would be an advantage.
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: The course covers issues of Machine Learning dealing with music and audio signals, including basic concepts in digital signal processing, MIDI, audio representation, analysis and feature extraction and temporal models including Markov and generative neural networks with applications for automatic music generation and sound synthesis. Students will be expected to complete a project in small groups and present a paper. Students of all backgrounds are welcome and musical skills are not required but would be an advantage.
Note: There is a synchronous only element for this course which is the project presentation in week 4. Students must be able to attend during the scheduled times.
Spring Session 2021
CSE 190 A00: The Environmental Impact of Modern Computing with George Porter
Prerequisite(s): None (application deadline has passed, no longer required)
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: Computing underpins much of modern life. Traditional devices such as laptops, desktops, smartphones, and tablets are now joined by compute-enabled smart cars, appliances, and the so-called "Internet of Things." Increasingly, these devices are enabled by cloud computing, hosted in large-scale Internet datacenters. The cloud hosts social media, entertainment, telepresence, and video and audio conferencing. Furthermore, cloud-hosted "AI" and "ML" have the potential to reinvent many traditional industries such as travel and logistics.
In this class, we will take a critical look at the 360-degree impact of modern computing technologies on the environment, asking the following questions:
- Where do these devices come from? What is the environmental impact of their manufacturing?
- How are these devices powered? What are their energy demands and where does that energy come from?
- Where do these devices go when we're done using them? What is e-Waste? What is the "circular economy?"
- What is the role of public policy and governments in managing and mitigating these environmental effects?
- How to communicate issues related to environmental impacts to the public?
- What can you, as a student, do now to help address the environmental impact of modern computing?
In this course, we will read and discuss books and articles on these topics, interspersed with guest speakers who have unique insights into the technological, scientific, and policy-making aspects of environmental computing impact. In addition to actively participating in class discussion, students will undertake a substantial research project to investigate some aspect of the environmental impact of computing, culminating in a research paper and content aimed at educating the public on the project's topic.
CSE 190 B00: Virtual Reality Technology with Jurgen Schulze
Prerequisite(s): CSE 167 or MATH 155A
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: Virtual reality (VR) and Augmented Reality (AR) have been capturing people’s imagination for decades, but only in recent years has it been possible to build head-mounted displays inexpensive enough for the consumer market. This course aims to explain how VR and AR technology works, and the class is going to do various programming projects to better understand the potential and limitations of VR and AR technology.
The course is structured into the following parts:
- Overview of the state-of-the-art VR and AR technologies and research trends
- Fundamental physics of 3D displays, including the major 3D depth cues.
- Discussion of most commonly used display types such as LCDs and OLEDs: display materials, device structures, working principles, research trends.
- How to create stereographic images.
- How the human eye sees 3D images: includes monoscopic and stereoscopic depth cues, motion parallax, and the physiology of the eye itself.
- Immersive VR systems: from head-mounted displays to walk-in CAVE systems.
- Challenges with today’s consumer-level VR and AR systems.
- Programming projects to get hands-on experience with fundamental VR and AR rendering techniques, such as 3D stereo, eye separation, frame rate, 3D controllers, etc.
Course delivery: Completely remote course BUT students will need to have a VR headset with controllers (loaner units will be available).
CSE 190 C00: Race, Gender, and Computing with Christine Alvarado and Joseph Politz
Important course notes: CANCELED
Our CSE 194 course titled "Race, Gender, and Computing" has been approved by the Academic Senate and the course is now available to waitlist via WebReg. Because of this, we will be officially canceling this section of CSE 190 (Section C00) for Spring 2021. If you are still interested in taking this course for Spring quarter, feel free to add yourself to the CSE 194 waitlist via WebReg. Please note, we cannot retain your waitlist position from one course to another.
Students who were officially enrolled in SP21 CSE 190 C00 will have until March 22nd to complete their enrollment in SP21 CSE 194. After that, any available seats will be released for waitlisted to enroll as space permits (in waitlist order). If you have any additional questions, please feel free to contact us via the Virtual Advising Center (vac.ucsd.edu).
DEI Notes: Conditionally approved as a DEI course (still pending official approval, so not 100% guaranteed yet). The reviewing committee requested additional documentation for consideration.
CSE 190 D00: Algorithms and systems for biomolecular big data (aka: Biomolecular big data systems) with Nuno Bandeira
Prerequisite(s): CSE 12 (CSE 100 and CSE 101 HIGHLY recommended)
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: The course will be held in real-time, asynchronous enrollment not supported.
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 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’.
Prior knowledge of molecular biology is not assumed and is not required; essential concepts will be introduced in the course as needed.
CSE 190 E00: Human-centered Computing for Health (HC4H) with Professor Weibel:
Prerequisite(s): CSE 170 or COGS 120, AND must be a graduating senior with a deep interest in research around technology and healthcare, and to have some experience in healthcare, technology or both.
To enroll: Submit course clearance request via Enrollment Authorization System (EASy) and include your academic status and justification for wanting to enroll in the course.
Description: HC4H an interdisciplinary course that brings together students from Engineering, Design, and Medicine, and exposes them to designing technology for health and healthcare. The course is focused on studying how technology is currently used in healthcare and identify opportunities for novel technology to be developed for specific health and healthcare settings.
In the first part of the course, students will be engaging in dedicated discussion around the design and engineering of novel solutions for current healthcare problems. These discussions will be catalyzed by in-depth online discussions and virtual visits with experts in a variety of healthcare domains such as emergency room physicians, surgeons, intensive care unit specialists, primary care clinicians, medical education experts, health measurement experts, bioethicists, and more. The second part of the year will focus on a design group project that will capitalize on the visits and discussions with the healthcare experts and will aim to propose specific technological solutions and present them to the healthcare stakeholders.
Successful students in this class often follow up on their design projects with the actual development of an HC4H project and its deployment within the healthcare setting in the following quarters
Link to past courses: http://hc4h.ucsd.edu/
CSE 190 F00: Human-AI Interaction with Kristen Vaccaro
Prerequisite(s): No course prerequisites, but all assignments will be in Python (students are expected to be proficient in Python if enrolling in this course)
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.
Winter Session 2021
CSE 190 A00: Wireless Embedded Systems with Professor Aaron Schulman (aka Aaron Shalev)
Prerequisite: CSE30 (CSE120 recommended)
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, Amplifiers, Regulators, Power supplies, 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.
CSE 190 B00: Introduction to Computing Education Research with Prof. Gerald Soosai Raj
Prerequisite: None
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: Computer Science as a major has high societal demand. In addition, computer programming is a skill increasingly important for all students, not just computer science majors. However, computer science remains a challenging field for students to learn. This course examines what we know about key questions in computer science education: Why is learning to program so challenging? What pedagogical choices are known to help students? What barriers do diverse groups of students (e.g., non-native English speakers) face while learning computing? How do those interested in Computing Education Research (CER) study and answer pressing research questions?
This course surveys the key findings and research directions of CER and applications of those findings for secondary and post-secondary teaching contexts. We study the development of the field, current modes of inquiry, the role of technology in computing, student representation, research-based pedagogical approaches, efforts toward increasing diversity of students in computing, and important open research questions. CER is a relatively new field and there is much to be done; an important part of the course engages students in the design phases of a computing education research study and asks students to complete a significant group project (e.g., a review of an area in computing education research, designing an intervention to increase diversity in computing, prototyping of a software system to aid student learning).
Fall Session 2020
CSE 190 A00: Algorithms and systems for biomolecular big data with Professor Nuno Bandeira
Prerequisites: CSE 12 (CSE 100 and CSE 101 HIGHLY recommended)
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 bases for a) interpretation of the data, b) testing pre-existing knowledge and c) detecting new discoveries. This course will cover these data science concepts with a focus on the use of biomolecular big data to study human disease. With the sequencing of the human genome and the subsequent identification of our list of parts (genes and their protein products), there is now an open quest towards understanding the many roles of the protein gene products using automated high-throughput approaches. Realizing the growing availability of hundreds of terabytes of data from a broad range of species and diseases, we will discuss 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). Prior knowledge of molecular biology is not assumed and is not required; essential concepts will be introduced in the course as needed.
CSE 190 B00: Post-Relational Data Models with Professor Alin Deutsch
Prerequisites: CSE 132A or approved equivalent
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: The course surveys a wide range of post-relational data models and high-level query languages that have achieved prominence with the advent of the Big Data era. These include graph database models and query languages in their various incarnations, such as XML and its standard query languages XPath and XQuery; JSON and its query languages SparkSQL, AQL, MongoDB's query language, etc.; RDF and Semantic Web data (SparQL); graph databases (neo4j's graph data model and the Cypher query language, the Gremlin query language, the upcoming GQL standard, etc.). The course emphasizes the common ideas across these models and languages, connecting them to their common roots in object-oriented and SQL databases. Attendants will be prepared to face any new model by learning how to classify its primitive along model-transcendent dimensions. They will also acquire practical data modeling and query programming skills required by today's data scientists.
CSE 190 FAQs: here