Machine Learning and Data Mining

Definitions of Machine Learning, Data Mining and the relationship between them; the CRISP Data Mining process model; major tasks including classification, regression, clustering, association learning, feature selection, and reinforcement learning; algorithms for these tasks that may include decision tree learning, instance-based learning, probabilistic learning, support vector machines, linear and logistic regression, and Q-learning; open-source software tools for data mining; practical applications such as sensor data analysis, healthcare data analysis, and text mining to identify spam email; ethical issues and emerging trends in data mining and machine learning.

Learning Outcomes
1.Define Machine Learning and Data Mining and discuss their relationship
2.Explain what major categories of Machine Learning tasks entail
3.Demonstrate how to apply the Data Mining process to practical problems
4.Explain and apply algorithms for decision tree learning, instance-based learning, linear and logistic regression, probabilistic learning, support vector machines, and reinforcement learning
5.Given a dataset and data mining task to be addressed, select, apply and evaluate appropriate algorithms, and interpret the results
6.Discuss ethical issues and emerging trends in data mining and machine learning