CSC 478 - Programming Data Mining Applications

DSC 478
Fall 2020

Syllabus

Course Material

Assignments

Class Project

Online Resources

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Schedule and Class Material

Week 1 - Sep 09, 2020

Lecture Material

Resources

Assignments/Readings

Topics:

*	Introduction to the Course
*	Overview of Data Mining and Knowledge Discovery Process
*	Brief Python review and an overview of Numpy
*	Brief Pandas Tutorial

*	Lecture Videos (D2L) 1- Brief Course Overview 2- Overview of Data Mining & KDD Process (32 mins) 3- Basics of Python & Numpy (44 mins) 4- Population Example - Part 1: Numpy (34 Mins) 5- Population Example - Part 2: Pandas (26 Mins)
*	Class Examples (Notebooks) - Python/Numpy Basics - Populations - Populations with Pandas
*	Related Files for Examples - populations.txt

*	Install and test Python distribution (ideally you should install the distributon from Anaconda which automaticaly installs all of the necessary libraries used in this class).
*	Familiarize yourself with IPython, and particularly, Jupyter Notebook. There is also a nice 30 min. Jupyter Notebook Tutorial Video by Corey Schafer.
*	Go through the "Quick Tutorial" on Numpy User Guide and try to follow the examples on your own (using Jupyter Notebook as the shell).
*	Review Section 1 of Python Scientific Lecture Notes.

Week 2 - Sep 16, 2020

Lecture Material

Resources

Assignments/Readings

Topics:

*	Brief Pandas Tuorial (Cont.)
*	Understanding Characteristics of Data [Slides] [Video (35 mins)]
*	Data Preparation and Preprocessing [Slides] [Video (50 mins)]

*	Lecture Videos (D2L) 1- Understanding Data Characteristics (35 mins) 2- Data Preparation & Preprocessing (50 Mins) 3- Preprocessing w/ Pandas Video Store Example (63 mins)
*	Examples (Notebooks) - Video Store with Pandas - Video Store (Missing Values)

*	Related Files for Examples - Video_Store.csv

*	Familiarize yourself with Pandas basics. A good place to start is the Pandas Tutorials page in Pandas Documentation. You might also review Python Pandas Tutorial: A Complete Introduction for Beginners.
*	In the Matplotlib User Guide, read the Matplotlib Pyplot Tutorial
*	Read Section 1.4 of the Python Scientific Lecture Notes on Matplotlib.

Week 3 - Sep 23, 2020

Lecture Material

Resources

Assignments/Readings

Topics:

*	Distances, Similarities, and K-Nearest-Neighbor Search

Review Material:

*	Classification & Prediction - Review of Basic Concepts Video (43 mins)] [Slides]

*	Lecture Videos (D2L) 1- Distances, Similarities, & KNN Classification (69 mins) 2- KNN Search Example Notebooks (35 mins) 3- Review of Classification Basic Concepts (43 mins) 4- KNN Classification - Video Store Example (33 mins)
*	Examples (Notebooks) - KNN Search Example 1 - KNN Search Example 2 - Video Store (KNN Classifier)

*	Read Chapter 2 of Machine Learning in Action (MLA).

Week 4 - Sep 30, 2020

Lecture Material

Resources

Assignments/Readings

Topic: Supervised Learning

*	Text Categorization

Review Material:

*	Decision Trees [Video (41 mins)] [Slides]

*	Bayesian Classification [Video (32 mins)] [Slides]

*	Lecture Videos (D2L) 1 - Text Categorization (46 mins) 2 - TFIDF and Document Categorization (21 mins) 3 -* Decision Tree Learning (41 mins) 4 - Bayesian Classification (32 mins) 5 - Classification using Scikit-learn (61 mins)
*	Examples (Notebooks) - TF*IDF and Document Categorization - Video Store (Scikit-learn)

*	Read Chapters 3 and 4 of MLA.
*	Read scikit-learn user guide: Sections: 1.2, 1.6, 1.9, 1.10, 4.3.

Week 5 - Oct 07, 2020

Lecture Material

Resources

Assignments/Readings

Topic: Supervised Learning

* Classification (continued)

* Notes on Assignment 2

* Notes on Personalization and Recommender Systems

*	Lecture Videos (D2L) 1 - Classification using Scikit-learn (61 mins) 2 - Notes on Assignment 2 (55 mins) - See PPT slides in the left column
*	Examples (Notebooks) - Video Store - Scikit-learn (Part 2) (Part 1 is posted above in Week 4)

*	Other Relevant Resources - Recommender Systems Wiki - Recommender-Systems.org

*	Read Chapter 8 of MLA.
*	Review scikit-learn user guide: Sections: 3.1, 3.3, 3.5.
*	Review scikit-learn user guide: Sections: 1.1 (Linear Models)
*	Read Recommender Systems Article in the Encyclopedia of Machine Learning
*	Read Wikipedia article on Collaborative Filtering

Week 6 - Oct 14, 2020

Lecture Material

Resources

Assignments/Readings

Topics: Supervised Learning

* Basic Regression Analysis

*	Model Selection & Optimization: - Gradient Descent Optimization - Feature Selection - Parameter Selection

*	Lecture Videos (D2L) 1- Basic Regressions Analysis (34 mins) 2 - Regression Using Scikit-learn (44 mins) 3 - Feature / Model Selection Strategies (46 mins) 4 - Gradient Descent Optimization (35 mins)
*	Examples (Notebooks) - Regression Analysis using Scikit-learn - Feature / Model Selection Strategies - Gradient Descent Optimization

*	Read Chapter 10 of MLA.
*	Review scikit-learn user guide: Sections: 1.5, 1.13, 3.2.
*	Review scikit-learn user guide: Sections: 2.3 (Clustering), and the API documentation for Kmeans.

Week 7 - Oct 21, 2020

Lecture Material

Resources

Assignments/Readings

Topic: Unsupervised Learning

*	Clustering

Video: Additional Notes on Assignment 3 (30 mins)
- PPT slides for the video

*	Lecture Videos (D2L) 1 - Clustering Concepts and Algorithms (29 mins) 2 - Clustering Example Jupyter Notebooks (34 mins) 3 - Additional Notes on Assignment 3 (30 mins) [PPT slides for the video]

*	Examples (Notebooks) - K-means Clustering - Document Clustering

*	Review Wikipedia pages on Cluster Analysis, including the article on Kmeans Clustering and Hierarchical Clustering.
*	Read Chapters 13 and 14 of MLA.

Week 8 - Oct 28, 2020

Lecture Material

Resources

Assignments/Readings

Topic: Unsupervised Learning

*	Principal Component Analysis

*	SVD (Singular Value Decomposition), Recommender Systems, and Matrix Factorization

*	Lecture Videos (D2L) 1- Principal Component Analysis - PCA (35 mins) 2- Singular Value Decomposition (22 mins) 3- Recommender Systems & Matrix Factorization (49 mins)

*	Examples (Notebooks) - Basic PCA Example - Document Clustering, PCA, and SVD - Item-Based Rec Test - Joking with Matrix Factorization

*	Read Chapters 11 and 14 of MLA.
*	Read: Matrix Factorization: A Simple Tutorial and Implementation in Python, by Albert Au Yeung.

Week 9 - Nov 04, 202

Lecture Material

Resources

Assignments/Readings

*	Support Vector Machines

Also:

- Using Machine Learning
Pipelines in Scikit-learn
- More examples of Model
Optimization

*	Lecture Videos (D2L): 1 - Support Vector Machines - Basics (24 mins) 2 - SVM - Jupyter Notebook Example (22 mins) 3 - Model Selection on News Group Data (28 mins)

*	Examples (Notebooks) - Support Vector Machines - Model Selection on Newsgroup Data

*	Review the Final Project Checklist.
*	Review scikit-learn user guide: Sections: 1.4 (Support Vector Machines).

Week10 - Nov 11, 2020

Lecture Material

Resources

Assignments/Readings

*	Ensemble Methods

*	Brief Course Summary

*	Lecture Videos (D2L) - Ensemble Methods

*	Examples (Notebooks) - Ensemble Classification

*	Review scikit-learn user guide: Sections: 1.11 (Ensemble Methods).
*	Review the Final Project Checklist.

Final Projects Due on Friday, Nov 20, 2020