Instructor: Alexander Dekhtyar, dekhtyar@csc.calpoly.edu, 14-212
Office Hours:
|
|
Who | Where | |
| Tuesday | 1:10pm - 3:00pm | Alex | 14-212 |
| Wednesday | 3:10pm - 4:00pm | Alex | 14-212 |
| Friday | 3:10pm - 4:00pm | 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 0 | Due: January 13 (Monday) | Distance Metrics (and Python) | Postscript | [January 8, 2025] | ||
| Lab 1 | Due: January 20 (Tuesday) | K-Nearest Neighbors | Postscript | [January 12, 2025] | ||
| Lab 2 | Due: January 31 (Friday) | Classification: Decision Trees | Postscript | Data | [January 22, 2025] | |
| Lab 3 | Due: February 7 (Friday) | Classification: Random Forests | Postscript | Data | [January 31, 2025] | |
| Lab 4 Lab 4 Addendum |
Due: February 21 (Friday) | Unsupervised Learning
Cluster Evaluation |
Postscript
Addendum (Postscript) |
PDF
Addendum (PDF) |
Data | [February 10, 2025] |
| Lab 5 | Due: February 28 | Collaborative Filtering | Postscript | Data | [February 17, 2025] | |
| Lab 6 | Due: March 7 | Information Retrieval/Text Mining | Postscript | Data | [February 27, 2025] | |
| Lab 7 | Due: | Link Analysis | Postscript | Data | [March 7, 2025] |
| Analytical Project | Due: February 7/March 19 | Project Specification | Postscript | [February 7, 2025] | |
| Analytical Project: Final Deliverables | Due: March 19 | Project Specification | Postscript | [March 10, 2025] |
--->
| (January 6, Monday) | Lecture 1 | What is KDD? | Postscript | ||||
| (January 8, Wednesday) | Lecture 2 | Distance and Similarity Metrics | Postscript | ||||
| (January 10, Friday) | Lecture 3 | Introduction to Supervised Learning | Postscript | Iris-example.ipynb | |||
| (January 17, Friday) | Lecture 4 | Classification. Decision Trees | Postscript | ||||
| (January 27, Monday) | Lecture 5 | Classification: C4.5. example | Postscript | Sample dataset (CSV) | Decision Tree (JSON) | ||
| (January 29, Wednesday) | Lecture 6 | Classification: Ensemble Methods | Postscript | ||||
| (February 3, Monday) | Lecture 7 | Classification: Naive Bayes | Postscript | ||||
| (February 5, Wednesday) | Lecture 8 | Clustering: K-means | Postscript | KMeans Clustering Demo (.ipynb) | |||
| (February 12, Wednesday) | Lecture 9 | Evaluation of Clustering Methods | Postscript | ||||
| (February 12, Wednesday) | Lecture 10 | Clustering: Hierarchical | Postscript | ||||
| (February 12, Wednesday) | Lecture 11 | Clustering: Density-Based | Postscript | ||||
| (February 19, Wednesday) | Lecture 12 | Collaborative Filtering: Intro | Postscript | ||||
| (February 19, Wednesday) | Lecture 13 | Collaborative Filtering: Evaluation | Postscript | ||||
| (February 26, Wednesday) | Lecture 14 | Information Retrieval: measures, models | Postscript | ||||
| (February 26, Wednesday) | Lecture 15 | Information retrieval: extending VSM | Postscript | ||||
| (March 3, Monday) | Lecture 16 | Graph Mining | Postscript | ||||
| (March 5, Wednesday) | Lecture 17 | PageRank:The Algorithm | Postscript | ||||
| (March 5, Wednesday) | Lecture 18 | PageRank: The Math | Postscript | ||||
| (MArch 10, Monday) | Lecture 19 | Association Rule Mining: Apriori | Postscript | ||||
| (MArch 10, Monday) | Lecture 20 | Association Rule Mining Example | Postscript |