SChedule

• Thurs (8/27): Introduction to Data Mining (slides)

Supplemental:

• Chapter 1, Introduction to Data Mining [pdf]

• TED Talk: How to stand out in an AI world by Sol Rashidi (15-min video)

• Tues (9/1): Data Basics (pdf)

• Thur (9/3):  Data Exploration (pdf)

  • Data URL

Supplemental:

• Chapter 2, Data [pdf]

• Tues (9/8): HW1 Studio (optional)

  • What is HW studio? In class time to work on HW

• Thur (9/10): Data Preprocessing (pdf)

Supplemental:

• Kang, The Prevention and handling of missing data, Korean J. Anes. 2013 

• Tues (9/15): Data Pre-processing + Project Intro (slides)

  • Mandatory Attendance for Project Intro

  • Project Teams

• Thur (9/17): Discovering & Evaluating Datasets (slides)

  • Project Kick Off | Attendance Mandatory

Supplemental:

• Practice tutorials and surveys for getting starting with a dataset

• Example Open Datasets

• Gong et al., "A survey on dataset quality in machine learning," Information & Software Tech, 2023

• Tues (9/22): Frequent Pattern Mining I

• Thur (9/24): Frequent Pattern Mining II

Supplemental:

• Diaz Garcia et al., A survey on the use of association rule mining techiniques in textual social media, Artificial Intelligence Review 2022 

• Tues (9/29): Classification Basics I

• Thur (10/1): Classification Basics II

Supplemental:

• TBD

• Tues (10/6): Clustering Basics I

• Thur (10/8): Clustering Basics II

Supplemental:

• TBD

• Tues (10/13): Fall Break!

• Thur (10/15): Review for Midterm

Supplemental:

• TBD

• Tues (10/20): Midterm 

• Thurs (10/22): Clustering Basics III