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University
Databases and Data Mining
Course Information
| Course | Database and data mining I | Code | MBDM 1-3 14 |
|---|---|---|---|
| Directions | 70230801 – Computer Linguistics (Master) | Semester | 1, 2 |
| Type of subject | Elective | Taught Language | English |
| Lectures | 40 | Practical Lessons | 80 |
| Subject Teacher | dr. Jamolbek Mattiev | Independent Work | 180 |
| Total Hours | 300 | Credits | 10 |
Lectures – Semester I
| Code | Topic | Material |
|---|---|---|
| M1 | Introductory lecture: Basic concepts and definitions | Download |
| M2 | Application fields of data mining | Download |
| M3 | The importance of data generation and big data | 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) and one rule classifier (OneR) | Download |
| M9 | Classification (Supervised machine learning techniques) Part I: Naïve Bayes | Download |
Practical Lessons – Semester I
| Code | Topic | Material |
|---|---|---|
| A1 | Installing the WEKA software | Download |
| A2 | Exploring the WEKA workbench | Download |
| A3 | The CRISP-DM standard | Download |
| A4 | Downloading the dataset from UCI Machine Learning repository | Download |
| A5 | The Generation of sample datasets | Download |
| A6 | Manual classification data generation | Download |
| A7 | Analysing the big data | Download |
| A8 | Analyzing the input concepts by sample dataset on WEKA | Download |
| A9 | Development of sample classification data and applying to WEKA | Download |
| A10 | Using WEKA to load and visualize sample data sets – understanding the ARFF format | Download |
| A11 | Applying some statistical methods on WEKA | Download |
| A12 | EDA and transforming the data by using WEKA | Download |
| A13 | Developing simple classification model by using sample data | Download |
| A14 | Applying simple classification (ZeroR and OneR) algorithms to sample datasets | Download |
| A15 | Applying simple classification (Naive Bayes) algorithms to sample datasets | Download |
| A16 | Applying cross-validation method in WEKA | Download |