Sample Syllabus


Catalog Description: The course introduces fundamental and practical tools, techniques, and algorithms for Knowledge Discovery and Data Mining (KD&DM). It provides a balanced approach between methods and practices. On the methodological side, it covers the key techniques for transforming data into business intelligence including: Data Preprocessing, Data Quality, K-Nearest Neighborhood Algorithm, Machine Learning (ML) and Decision Trees (DT: C4.5, and CART), Artificial Neural Networks (ANN), Clustering, and Algorithm Evaluation Techniques. On the practical side, a number of case studies from the real world applications are analyzed and discussed to illustrate the practical significance of the various techniques and reinforce learning. A current list of KD&DM software products and algorithms are also introduced.  Prerequisite: Knowledge of probability.

Textbook(s)

Required:
1.      Discovering Knowledge in Data: An introduction to Data Mining, Daniel T. Larose, John Wiley, 2005
2.   Lecture Notes and Handouts

Recommended:


Week-By-Week
 There will be weekly exercises and bi-weekly projects/case studies.
Week
 		Topics Covered
 		Reading
 		Assignments
1
  1. What is Data Mining & Knowledge Discover?
  2. The Six Phases of  Data Mining
Ch. 1
2

Five Business and Operations Applications

handout
3
  1. Data Cleaning
  2. Handling Missing Data
  3. Identifying Misclassifications
Ch. 2
 		case study 1
4

  1. Graphical Methods for Outliers

  2. Data Transformation: Min-Max Normalization; Z-Score Standardization
 		Handout
5
  1. Supervised and Unsupervised Learning
  2. Methodology for Supervised Learning
  3. k-Nearest Neighbor Algorithm
  4. Distance Function
  5. Database Considerations

Ch. 5, handout.
 		case study 2
6
  1. k-Nearest Neighbor Algorithm for estimation and prediction
  2. Choosing k
  3. Case Study

Ch 5
7
  1. C4.5 Algorithm
  2. Classifications and Regression Trees (CART) Algorithm
Ch. 6
 		case study 3
8
  1. Decision Rules
  2. Comparison of the C4.5 and CART Algorithms Applied to Real Data
  3. Case Studies
Ch. 6
9
  1. Human Braine
  2. Input and Output
  3. Neural Network for Estimation and prediction
  4. Summation Function
  5. Sigmoid Activation Function
Ch. 7
 		case study 4
10
  1. Back-Propagation Algorithm
  2. Terminating Criteria
  3. Learning Rate
  4. Applications of ANN
  5. Case Study
Ch. 7
11
  1. Clustering Task
  2. Hierarchical Clustering Methods
  3. k-Means Clustering
Ch. 8
 		case study 5
12
  1. Applications of k-Means Clustering
  2. Applications of k-Means Clustering Using SAS Enterprise Miner
  3. Case Study

Ch. 8
13
 		Model Evaluation Techniques
 		Handout
 		case study 6
14

Projects and Papers Presentations 

An End-to-End Knowledge Discovery and Data Mining Project developed and executed during the semester by each students using a real world data set. The result is documented as a research project and presented at the class.