Urganch Davlat Universiteti - Data Mining (Master)

Abu Rayhon Beruniy nomidagi Urganch davlat universiteti

Data Mining (Master)

Course	Data mining	Code	DM1-KMQUT243
Directions	70610501 – Artificial Intelligence (Master)	Semester	1
Type of subject	Elective	Taught Language	English
Lectures	30	Practical Lessons	46
Subject Teacher	dr. Jamolbek Mattiev	Independent Work	104
Total Hours	180	Credits	6

Code	Topic	Material
M1	Introductory lecture: Basic concepts and definitions	Download
M2	Areas of application of data mining and machine learning	Download
M3	The CRISP-DM standard	Download
M4	Input concepts	Download
M5	Understanding and visualizing the data in different formats	Download
M6	Introduction to statistics	Download
M7	Data preparation (discretization, normalization, balancing, …)	Download
M8	Classification (Supervised machine learning techniques) Part I: Majority classifier (ZeroR), one rule classifier (OneR) and Naïve Bayes	Download
M9	Classification Part II: decision trees	Download
M10	Classification Part III: decision trees—C4.5 algorithm	Download
M11	Classification Part III: decision rules	Download
M12	Clustering (Unsupervised machine learning techniques): K-means and Hierarchical Agglomerative clustering algorithms	Download
M13	Evaluation and assessment of the learnt models	Download
M14	Regression and nearest neighbor	Download
M15	Association Rule Mining (Unsupervised machine learning techniques): frequent pattern mining and association rule discovery	Download

Code	Topic	Material
A1	Installing the WEKA software	Download
A2	Exploring the WEKA workbench	Download
A3	Doing exercises with Weka	Download
A4	Downloading the dataset from UCI Machine Learning repository	Download
A5	Analyzing the input concepts by sample dataset on Weka	Download
A6	Using WEKA to load and visualize sample data sets – understanding the ARFF format	Download
A7	Applying some statistical methods on Weka	Download
A8	Finding the missing values and outliers using Weka	Download
A9	EDA and transforming the data by using WEKA	Download
A10	Applying simple classification algorithms (ZeroR, oneR, Naive Bayes) to sample data sets	Download
A11	Learning decision trees from sample data with WEKA	Download
A12	Executing the C4.5 algorithm on Weka	Download
A13	Executing the decision rules algorithms on Weka	Download
A14	Using different clustering methods to describe data. Experiments on WEKA	Download
A15	Doing exercises on K-means clustering algorithm	Download
A16	Evaluating and comparing different machine learning algorithms on different datasets in WEKA. Performing statistical significance testing	Download
A17	Learning cross-validation and percentage split method on Weka	Download
A18	Linear regression and KNN algorithms on Weka	Download
A19	Finding frequent patterns and association rules in sample data using WEKA	Download
A20	classification of association rules on Weka	Download