CS545 - ML for Data Mining / CS407 - Machine Learning

Summer 2024 (online asynchronous)

Prerequisites by topic

• Programming Fundamentals and Data Structures
• Basic Statistics
• Linear Algebra
• Discrete Mathematics

Textbook

Software

Course Goals

• Introduce students to the basic concepts and techniques of Data Mining and Machine Learning.
• Develop skills of using recent Machine Learning software for solving practical problems.
• Gain experience of doing independent study and research.

Grading Policies

Late assignments will be marked one letter grade down for each 3 days they are late. It is expected that all students will conduct themselves in an honest manner and NEVER claim work which is not their own. Violating this policy will result in a substantial grade penalty or a final grade of F.
 

Course Content (12 units)

1. Introduction
• What is data mining?
• Related technologies - Machine Learning, DBMS, OLAP, Statistics
• Data Mining Goals
• Stages of the  Data Mining Process
• Data Mining Techniques
• Knowledge Representation Methods
• Applications
• Example: weather data
2. Data Warehouse and OLAP
• Data Warehouse and DBMS
• Multidimensional data model
• OLAP operations
• Example: loan data set
3. Data preprocessing
• Data cleaning
• Data transformation
• Data reduction
• Discretization and generating concept hierarchies
• Installing Weka 3 Data Mining System
• Experiments with Weka - filters, discretization
4. Data mining knowledge representation
• Task relevant data
• Background knowledge
• Interestingness measures
• Representing input data and output knowledge
• Visualization techniques
• Experiments with Weka - visualization
5. Attribute-oriented analysis
• Attribute generalization
• Attribute relevance
• Class comparison
• Statistical measures
• Experiments with Weka - using filters and statistics
6. Learning Association rules
• Motivation and terminology
• Example: weather data
• Basic idea: item sets
• Generating item sets and rules efficiently
• Correlation analysis
• Experiments with Weka - mining association rules
7. Machine Learning algorithms: Classification
• Basic learning/mining tasks
• Inferring rudimentary rules: 1R algorithm
• Decision trees
• Covering rules
• Experiments with Weka - decision trees, rules
8. Machine Learning algorithms: Prediction
• The prediction task
• Statistical (Bayesian) classification
• Bayesian networks
• Instance-based methods (nearest neighbor)
• Linear models
• Experiments with Weka - Prediction
9. Evaluating what's been learned
• Basic issues
• Training and testing
• Estimating classifier accuracy (holdout, cross-validation, leave-one-out)
• Combining multiple models (bagging, boosting, stacking)
• Minimum Description Length Principle (MDL)
• Experiments with Weka - training and testing
10. Mining real data
• Preprocessing data from a real medical domain (310 patients with Hepatitis C).
• Applying various data mining techniques to create a comprehensive and accurate model of the data.
11. Machine Learning algorithms: Clustering
• Basic issues in clustering
• First conceptual clustering system: Cluster/2
• Partitioning methods: k-means, expectation maximization (EM)
• Hierarchical methods: distance-based agglomerative and divisible clustering
• Conceptual clustering: Cobweb
• Experiments with Weka - k-means, EM, Cobweb
12. Text and Web Mining
• Basics of Information Retrieval
• TFIDF representation of text documents
• Text classification