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

- For sections where enrollment is via EASy clearance, non-CSE majors will be cleared after the CSE Major Priority deadline


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.