DSC 478
 Spring 2020

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Schedule and Class Material


Week 1 - Mar 31, 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 Tuorial
*  Lecture Videos (D2L)
- Brief Course Overview
- Intro - Part 1 (54 mins)
- Intro - Part 2 (75 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 - Apr 07, 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)
- More Pandas (26 mins)
- Pandas Video Store (53 mins)
*  Examples (Notebooks)
- Video Store with Pandas
- Video Store (Missing Values)
*  Related Files for Examples
- Video_Store.csv
*  Assignment 1 is available. Due date: Sunday, April 19.
*  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 - Apr 14, 2020
Lecture Material Resources Assignments/Readings
Topics: 
 
*  More on Python for data analysis and visualization
*  Distances, Similarities, and K-Nearest-Neighbor Search



*  Lecture Videos (D2L)
- Distances & Similarities
  (74 mins)
- KNN Notebooks (72 mins)
*  Examples (Notebooks)

- KNN Search Example 1
- KNN Search Example 2

- Video Store (KNN Classifier)

 
*  Read Chapter 2 of Machine Learning in Action (MLA).
*  Assignment 1 is Due Sunday, April 19.
Week 4 - Apr 21, 2020
Lecture Material Resources Assignments/Readings
Topic: Supervised Learning
 
*  Classification & Prediction - Review of Basic Concepts
*  Text Categorization

Review Material:
*  Decision Trees  
[Video (41 mins)] [Slides]
*  Bayesian Classification 
[Video (32 mins)] [Slides]
*  Lecture Videos (D2L)

- Classification/Prediction
  (47 mins)

- Text Categorization (45 mins)

- Classification with scikit-learn
   (52 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 - Apr 28, 2020
Lecture Material Resources Assignments/Readings
Topic: Supervised Learning
*  Classification (continued)
 
*  Notes on Assignment 2
[Video (32 mins)] [Slides]
 
*  Notes on Personalization and Recommender Systems



*  Examples (Notebooks)

- Video Store - Scikit-learn
   (Part 2)
   (See: Classification with
   scikit-learn video posted 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 - May 05, 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)

- Regression Analysis (36 mins)

- Regression using scikit-learn
  (49 mins)

- Feature / Model Selection
   (49 mins)

- Gradient Descent
  Optimization (37 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 - May 12, 2020
Lecture Material Resources Assignments/Readings
Topic: Unsupervised Learning
*  Clustering



*  Lecture Videos (D2L)

Clustering Concepts & Algorihtms
 
Clustering Examples - Jupyter Notebooks (see below for the Notebooks covered in this 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 - May 19, 2020
Lecture Material Resources Assignments/Readings
Topic: Unsupervised Learning
*  Principal Component Analysis
*  SVD (Singular Value Decomposition),  Recommender Systems, and Matrix Factorization

*  Lecture Videos (D2L)

Principal Component Analysis (39 mins)

Singular Value Decomposition (26 mins)

Recommender Systems & Matrix Factorization (55 mins)

Recommender Systems & The Netflix Challenge (30 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 - May 26, 202
Lecture Material Resources Assignments/Readings
 
*  Support Vector Machines

*  Lecture Videos (D2L)

Support Vector Machines - Concepts (28 mins)

Support Vector Machines - Jupyter Notebook (28 mins) - See Below

Model Selection with News Group Data (32 mins) - See Below
*  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 - June 02, 2020
Lecture Material Resources Assignments/Readings
 
*  Ensemble Methods
*   Brief Course Summary
*  Lecture Videos (D2L)

Ensemble Classification (62 mins)
*  Examples (Notebooks)

- Ensemble Classification
*  Review scikit-learn user guide: Sections: 1.11 (Ensemble Methods).
*  Review the Final Project Checklist.
   
Final Projects Due on Tuesday, June 9, 2020
Copyright ©, Bamshad Mobasher, DePaul University.