CSI 382 - Data Mining and Knowledge Discovery

Course Outcomes

After completing the course, students are expected to be able to:

Demonstrate advanced knowledge of data mining concepts and techniques. (Knowledge and Application)
Apply the techniques of clustering, classification, association finding, feature selection and visualization on real world data (Application, Research, Innovation)
Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor, multiple regression, and neural networks.(Apply, Analyze and Design)
Determine whether a real world problem has a data mining solution and apply data mining software and toolkits to solve those problems. (Create and Evaluate)
Demonstrate knowledge of the ethical considerations involved in data mining. (Professionalism and Environmental Sustainability)

CSI 381 - Data Mining and Knowledge Discovery.pdf

Lecture Slides 1 - Introduction to Data Mining - Lec - 1.pdf

Lecture Slides 2 - Data Preprocessing - Lec - 2.pdf

Lecture Slides 3 - Exploratory Data Analysis - Lec - 3.pdf

Lecture Slides 4 - Statistical Inference - Lec - 4.pdf

Lecture Slides 5 - K-Nearest Neighbor Algorithm - Lec - 5.pdf

Lecture Slides 6 - Decision Trees - Lec - 6.pdf

Lecture Slides 7 - Neural Networks - Lec - 7.pdf

Lecture Slides 8 - Clustering Analysis - Lec - 8.pdf

Lecture Slides 9 - Model Evaluation Techniques - Lec - 9.pdf

Lecture Slides 10 - Association Mining - Lec - 10.pdf

Classes(5 weeks): 01 January – 6^th February 2022

Term 1 Exam: 7^th February – 16^th February 2022

Classes(5 weeks): 17^th February – 24^th March 2022

Term 2 Exam: 25^th March – 4^th April 2022

Classes(3 weeks): 5^th April – 27^th April 2022

Classes(2 weeks): 9^th May – 19^th May 2022

Final Term Exam: 26^th May – 9^th June 2022

Page updated

Google Sites

Report abuse