Instructor: Alexander Dekhtyar, dekhtyar@csc.calpoly.edu, 14-210
Office Hours:
|
|
Who | Where | |
| Monday | 10:10am - 11:00am | Alex | 14-210 |
| Tuesday | 9:10am - 11:00am | Alex | 14-210 |
| Wednesday | 9:10am - 11:00am | Alex | 14-210 |
Additional appoinments: send email.
Final Exam Date: Monday, December 13, 2019, 10:10 - 1:00pm
(note: there is no final exam, but we may use the time for course-related activities)
| Syllabus | Postscript | ||
| Github Id Survey | Survey | ||
| Jupyter Hub | Login Page | Log in with Gitgub Id | |
| Spring 2018 Exam files | Task1.ipynb, data1.csv | Task2.ipynb, data5.csv | Task3.ipynb, data8.csv |
| Lab 1 | Due: September 27 (Friday) | Insight From Data | Postscript | Data | [September 19, 2019] | |
| Lab 2 | Due: | Association Rules | Postscript | Data | [September 27, 2019] | |
| Lab 3 | Due: October 14 / October 22 (Mondays) | Supervised Learning (Classification) | Postscript | Data | [October 3, 2019] | |
| Lab 4 | Due: November 4 (Monday, 11:00am) | Unsupervised Learning | Postscript | Data | [October 23, 2019] | |
| Lab 5 | Due: November 15 | Information Retrieval/Text Mining | Postscript | Data | [November 2, 2019] | |
| Lab 6 | Due: November 25 | Collaborative Filtering | Postscript | Data | [November 15, 2019] | |
| Lab 7 | Due: December 6 | Link Analysis | Postscript | Data | [November 22, 2019] |
| Analytical Project | Due: November 9/December 13 2018 | Multiple Datasets | Postscript | [November 2, 2018] | |
| Ethics and KDD Assignment | Due: December 13, 2019 | Science Fiction Dystopia Story | Postscript | [November 22, 2019] |
Lab Data
| Lecture 1 | What is KDD? | Postscript | PowerPoint(by Jonathan Ventura) | Lecture 2 | Association Rules Mining: Apriori | Postscript | PowerPoint(by Jonathan Ventura) |
| Lecture 3 | Association Rules Mining: Apriori examples | Postscript | Lecture 4 | Classification. Decision Trees | Postscript |
| Lecture 5 | Classification: C4.5. example | Postscript | |||
| Lecture 6 | Classification: Beyond C4.5. | Postscript | |||
| Lecture 7 | Clustering: K-means | Postscript | |||
| Lecture 8 | Distance Measures | Postscript | |||
| Lecture 9 | Clustering: Hierarchical | Postscript | |||
| Lecture 10 | Collaborative Filtering: Intro | Postscript | Lecture 11 | Collaborative Filtering: Evaluation | Postscript |
| Lecture 12 | Information Retrieval: measures, models | Postscript | |||
| Lecture 13 | Information retrieval: extending VSM | Postscript | |||
| Lecture 14 | Social Network/Graph Mining | Postscript | |||
| Lecture 15 | PageRank:The Algorithm | Postscript | |||
| Lecture 16 | PageRank: The Math | Postscript | |||
| Lecture 17 | Community Discovery | Postscript | |||
| Lecture 18 | Naive Bayes | Postscript |