Updated: February 22, 2022
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 2022
S122 and S222 CSE 190 A00: Machine Learning for Music and Audio with Shlomo Dubnov
Prerequisites: (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.
Spring 2022
CSE 190 (A00): Introduction to Human-Centered Computing for Health (HC4H) with Nadir Weibel
Prerequisites: CSE 170 or COGS 120 or permission from the instructor
To enroll: Submit course clearance request via Enrollment Authorization System (EASy)
Description: The focus of HC4H is to learn how to use Human-Centered Design (HCD) to design and develop technology at the intersection of computer science and health. Students will learn regulations, ethical protocols, and methodologies to help them bridge technology and health. By the end of the class, students will have developed a design prototype and proposed solution to address a real-world problem.
This course is designed to develop an in-depth and comprehensive understanding of what it means to introduce and study technology across health and healthcare. Students will be exposed to a variety of real-world examples, gain a user-centered understanding from multiple points of view, and develop the skills needed to design solutions to solve real problems.
In the in-class part of the course, students will be engaging in dedicated discussion around 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 rooms physicians, surgeons, intensive care unit specialists, primary care clinicians, medical education experts, health measurement experts, bioethicists, and more.
The second part of the class 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. Students will be exposed to specific Human-Centered Design methods in class, and will be expected to deliver a web-page describing their final solution.
Successful students in this class often follow up on their design projects with actual development of an HC4H project and its deployment within the healthcare setting in the following quarters
If COVID regulation will allow us, students will be exposed to the health domain at large through presentations, in-person visits and discussions with experts in emergency rooms, trauma rooms, operating rooms, radiology clinics, sleep clinics, outpatient medical offices, the Simulation Training Center (STC), the Professional Development Center (PDC), the Center for the Future of Surgery (CFS), the Exercise and Physical Activity Resource Center (EPARC), and the West Health Institute.
If COVID won’t allow us to organize in-person visits, we will engage with our experts through remote panels and discussions, and “remote visits” to the same facilities.
Winter 2022
CSE 190 A00: Algorithms and Systems for Biomolecular Big Data
Prerequisites: CSE 12 (CSE 100 and CSE 101 are 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.
Fall 2021
CSE 190 A00: Cancelled
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 C00: 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.