Time: Tuesdays, 11 to 11:50am
Location: MSTB 110
Instructor:
Professor Padhraic Smyth
Description:
This is a weekly seminar class intended as an introduction to the field of Data Science.
The course is required for Data Science majors.
Format:
Each week will consist of a lecture
on a particular topic related to Data Science,
with lectures from faculty experts in areas such as statistics, computer science, and
application areas where data science is used. To pass this class you need to attend
and submit weekly reports for at least 8 of the 9 lectures from weeks 2 to 10.
Grading: Pass/Fail
Online copies of lecture slides
Copies of weekly review forms (from week 2 onwards, to be submitted each week to EEE dropbox by noon on the day of class)
| Date | Speaker | Department/Organization | Topic |
|---|---|---|---|
| Jan 9 | Padhraic Smyth | Computer Science | Introduction to Data Science |
| Jan 16 | Padhraic Smyth | Computer Science | Classification Algorithms in Machine Learning |
| Jan 23 | Michael Carey | Computer Science | Databases and Data Management |
| Jan 30 | Sameer Singh | Computer Science | Statistical Natural Language Processing |
| Feb 6 | Zhaoxia Yu | Statistics | An Introduction to Cluster Analysis |
| Feb 13 | Erik Sudderth | Computer Science | Computer Vision and Machine Learning |
| Feb 20 | John Brock | Cylance, Inc | Data Science and CyberSecurity |
| Feb 27 | Video Lecture (Kate Crawford) | Microsoft Research and NYU | Bias in Machine Learning |
| Mar 6 | Matt Harding | Economics | Data Science in Economics and Finance |
| Mar 13 | Padhraic Smyth | Computer Science | Review: Past and Future of Data Science |
Academic Integrity: It is the responsibility of each student to be familiar with UCI's Academic Integrity Policies and UCI's definitions and examples of academic misconduct. Failure to adhere to these policies below can result in a student receiving a failing grade in the class.