Instructor: Alexander Dekhtyar, dekhtyar@csc.calpoly.edu, 14-212
Office Hours:
|
Who | Where | |
Monday | 11:00am - 12:00am | Alex | 14-212 |
Tuesday | 1:00pm - 3:00pm | Alex | 14-212 |
Wednesday | 10:00am - 11:00am | Alex | 14-212 |
Additional appoinments: send email.
Syllabus | Postscript | ||
Jupyter Labs Server | Login Page | Log in with Cal Poly credentials | |
Sample Lab Test files | Task1.ipynb, data1.csv | Task2.ipynb, data5.csv | Task3.ipynb, data8.csv |
Sample Lab Test Solutions | Task1-solution.ipynb | Task2-solution.ipynb | Task3-solution.ipynb |
Lab 1 | Due: September 28 (Tuesday) | Insight From Data | Postscript | Data | [September 25, 2023] | |
Lab 2 | Due: October 9 (Monday) | Association Rules | Postscript | Data | [September 29, 2023] | |
Lab 3 | Due: October 20 (Friday) - Part 1 October 27 (Friday) - Part 2 | Supervised Learning (Classification) | Postscript | Data | [October 11, 2023] | |
Lab 4 | Due: November 10 (Friday) | Unsupervised Learning | Postscript | Data | [November 1, 2023] | |
Lab 5 | Due: November 27 | Collaborative Filtering | Postscript | Data | [November 13, 2023] | |
Lab 6 | Due: December 4 (Monday) | Information Retrieval/Text Mining | Postscript | Data | [November 17, 2023] | |
Lab 7 | Due: December 12 (Tuesday) | Link Analysis | Postscript | Data | [December 6, 2023] |
Analytical Project | Due: December 13 (multiple deadlines) | Project Specification | Postscript | [November 1, 2023] |
Lecture 1 | What is KDD? | Postscript | Lecture 2 | Association Rules Mining: Apriori | Postscript | Apriori Example (PDF) | Apriori Example (Googledoc, read-only) |
Lecture 3 | Association Rules Mining: Apriori examples | Postscript | Sample dataset (CSV) | Lecture 4 | Classification. Decision Trees | Postscript |
Lecture 5 | Classification: C4.5. example | Postscript | Sample dataset (CSV) | Decision Tree (JSON) | ||
Lecture 6 | Classification: Beyond C4.5. | Postscript | ||||
Lecture 6.5 | Predictive Linear Regression | Postscript | ||||
Lecture 7 | Clustering: K-means | Postscript | ||||
Lecture 8 | Distance Measures | Postscript | ||||
Lecture 9 | Clustering: Hierarchical | Postscript | ||||
Lecture 10 | Clustering: Density-Based | Postscript | ||||
Lecture 11 | Collaborative Filtering: Intro | Postscript | Lecture 12 | Collaborative Filtering: Evaluation | Postscript | |
Lecture 13 | Information Retrieval: measures, models | Postscript | ||||
Lecture 14 | Information retrieval: extending VSM | Postscript | ||||
Lecture 15 | Social Network/Graph Mining | Postscript | ||||
Lecture 16 | PageRank:The Algorithm | Postscript | ||||
Lecture 17 | PageRank: The Math | Postscript | ||||
Lecture 18 | Community Discovery | Postscript | ||||
Lecture 19 | Naive Bayes | Postscript |