DSC 478
 Fall 2022

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Schedule and Class Material


Week 1 - Sep 7 - Sep 13
Lecture Material Resources Assignments/Readings
Topics: 
 
*  Introduction to the Course
*  Overview of Machine Learning, Data Mining & the 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, ML  & KDD Process (41 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 14 - Sep 20
Lecture Material Resources Assignments/Readings
Topics: 
 
*  Understanding Characteristics of Data
 
*  Data Preparation and Preprocessing
   


*  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 21 - Sep 27
Lecture Material Resources Assignments/Readings
Topics: 
 
*  Distances, Similarities, and K-Nearest-Neighbor Search


Review Material:
*  Classification & Prediction - Review of Basic Concepts
*  Lecture Videos (D2L)
1- Review of Classification Basic Concepts (48 mins)
2- Distances, Similarities, & KNN Classification (69 mins)
3- KNN Search Example Notebooks (35 mins)
4- KNN Classification - Video Store Example (47 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 28 - Oct 4
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 - TF*IDF and Document  Categorization (21 mins)
3 - Review: Decision Tree Learning (41 mins)
4 - Review: Bayesian Classification (32 mins)
5 - Classification using Scikit-learn (61 mins)
*  Examples (Notebooks)

- TF*IDF and Document
   Categorization

- Video Store (Scikit-learn)

- Video Store - Scikit-learn
   (Part 2)
*  Read Chapters 3 and 4 of MLA.
*  Read scikit-learn user guide: Sections: 1.2, 1.6, 1.9, 1.10, 6.3.
Week 5 - Oct 5 - Oct 11
Lecture Material Resources Assignments/Readings
Topic: Supervised Learning
*  Classification (continued)
 
*  Overview of Recommender Systems
 
*  Notes on Assignment 2



*  Lecture Videos (D2L)

6 - Overview of Recommender Systems (72 mins)
*  Other Relevant Resources
- Recommender Systems Wiki
- Recommender-Systems.org
*  Read Chapter 8 of MLA.
*  Review scikit-learn user guide: Sections: 3.1, 3.3
*  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 12 - Oct 18
Lecture Material Resources Assignments/Readings
Topics: Supervised Learning
*  Basic Regression Analysis
*  Model Selection & Optimization:
- Gradient Descent
  Optimization
- Feature Selection
- Parameter Optimization




*  Lecture Videos (D2L):

1- Basic Regressions Analysis (34 mins)
2 - Feature Selection & Model Optimization (46 mins)
3 - 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: 1.1, 1.5, 3.2.
*  Review scikit-learn user guide: Sections: 2.3 (Clustering), and the API documentation for Kmeans.
 
Week 7 - Oct 19 - Oct 25
Lecture Material Resources Assignments/Readings
Topic: Unsupervised Learning
*  Clustering

* New: Additional Notes on Assignment 3
*  Lecture Videos (D2L)

1 - Clustering: Basic Concepts and Algorithms (40 mins)

2 - Clustering Examples - Jupyter Notebooks (34 mins)
*  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 26 - Nov 1
Lecture Material Resources Assignments/Readings
Topic: Unsupervised Learning
*  Principal Component Analysis
*  SVD (Singular Value Decomposition) 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 2 - Nov 8
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 (24 mins)
2. SVM Jupyter Notebook Example (22 mins)
3. Model Selection on Newsgroup 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 9 - Nov 15
Lecture Material Resources Assignments/Readings
 
*  Ensemble Methods
*   Brief Course Summary
*  Lecture Videos (D2L)

1. Enssemble Methods (53 mins)
*  Examples (Notebooks)

- Ensemble Classification
*  Review scikit-learn user guide: Sections: 1.11 (Ensemble Methods).
*  Review the Final Project Checklist.
   
Final Projects Due on Wednesday, November 16, 2022
Copyright ©, Bamshad Mobasher, DePaul University.