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Data Mining (Master)

Data Mining (Master)

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Course Information

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

Lectures – Semester I

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

Practical Lessons – Semester I

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
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