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Classification 101: Understanding Models and Methods for Data Mining

This course covers basic concepts of classification in data mining, including model construction and evaluation techniques. Students will gain knowledge on various classification methods and how to measure accuracy. Key topics include decision tree induction, Bayes classification, and rule-based methods. At the end of the course, students will be able to construct classification models, assess their accuracy, and apply supervised and unsupervised learning techniques. The course also emphasizes the importance of model evaluation and selection to improve classification accuracy.

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Classification 101: Understanding Models and Methods for Data Mining

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  1. Classification: Basic Concepts

  2. Course Objectives • At the end of the course students are able to • Familiarize with basic concepts related to classification – Data Mining functionality • Understand the process involved in construction of classification model • Familiarize with various classification methods • Methods to measure the accuracy of a classifier

  3. Classification: Basic Concepts Construction of classification model Asses and test the model Supervised and Unsupervised Learning ClassificationMethods Decision Tree Induction Bayes Classification Methods Rule-Based Classification Process in measuring the accuracy of classification model Model Evaluation and Selection Techniques to Improve Classification Accuracy: Ensemble Methods Summary Course Outline  3

  4. Assessment strategy Upon completion of the lecture students are instructed to • Complete the Feedback form on visual presentation • Students are given a quiz exam for 5 marks on the lecture discussed and to be written in a class room only. • Students have to implement one of the classification method as exercise and submit through MOODLE which carries 5 marks weightage under practical marks.

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