| Academic Unit: |
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| Mode of Delivery: |
Face to face |
| Prerequisites: |
None |
| Language of Instruction: |
English |
| Level of Course Unit: |
Undergraduate |
| Course Coordinator: |
- - |
| Course Lecturer(s): |
Rahim DEHKHARGHANİ |
| Course Objectives: |
This course aims to provide a general and also task-specific knowledge of analyzing any type of data in order to extract useful information. Students will practically work on a problem using mining techniques to extract the mentioned information. They can work on any type of data such as transactional data, textual data, audio, video or image data. At the end of the course, students are expected to be a data miner who knows different types of data and their attributes and is able to preprocess the data to make it ready for the main task. Finally they will be able to write code to extract the mentioned useful information from different types of data. |
| Course Contents: |
The course will cover topics such as knowing the data and attributes of data, preprocessing the data, association rule mining, classification, regression, clustering, decision trees, Naïve Bayes method and other machine learning algorithms. |
| Learning Outcomes of the Course Unit (LO): |
- 1- Ability to Know, Preprocess, and analyzing the data.
- 2- Ability to visualize the data.
- 3- Ability to Understand and analyze supervised/unsupervised learning methods such as classification, clustering, and regression.
- 4- Ability to use a programming language such as Python and its libraries to perform classification, clustering, regression, pattern mining, etc., on data.
|
| Planned Learning Activities and Teaching Methods: |
In-class learning. Weekly guests (from data mining companies). Programming homeworks done individually. Design and implementation of solution for a real-world problem as a group-work. |
| Week | Subjects | Related Preperation |
| 1 |
Introduction to data mining and python |
Lecture slides and reading material |
| 2 |
Python and Numpy library |
Lecture slides and reading material |
| 3 |
Python and Pandas library |
Lecture slides and reading material |
| 4 |
Python and matplotlib/seaborn libraries |
Lecture slides and reading material |
| 5 |
Know your data (data types, data dissimilarity measures, …) |
Lecture slides and reading material |
| 6 |
Data Preprocessing (cleaning, Integration, Reduction, Transformation, Discretization) |
Lecture slides and reading material |
| 7 |
Association Rule Mining |
Lecture slides and reading material |
| 8 |
Introduction to machine learning |
Lecture slides and reading material |
| 9 |
Machine learning and Classification |
Lecture slides and reading material |
| 10 |
Evaluation of classification systems |
Lecture slides and reading material |
| 11 |
Machine learning and Regression |
Lecture slides and reading material |
| 12 |
Machine learning and Decision Trees |
Lecture slides and reading material |
| 13 |
Machine learning and Naïve Bayes |
Lecture slides and reading material |
| 14 |
Machine learning and Clustering |
Lecture slides and reading material |
At Kadir Has University, a Semester is 14 weeks; The weeks 15 and 16 are reserved for final exams.
1) Data mining, techniques and concepts, Jiawei Han, Micheline Kamber, and Jian Pei, 3rd edition, Morgan Kaufmann, 2011
2) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Geron Aurelien, 2nd Edition, 2019
3) Python Data Science Handbook: Essential Tools for Working with Data 1st Edition
, by Jake VanderPlas, 2017, 2nd edition, Publisher: O'Reilly |
THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES (LO) AND PROGRAM QUALIFICATIONS (PQ)
| # |
PQ1 |
PQ2 |
PQ3 |
PQ4 |
PQ5 |
PQ6 |
PQ7 |
PQ8 |
PQ9 |
| LO1 |
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| LO2 |
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| LO3 |
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| LO4 |
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Contribution: 1 Low, 2 Average, 3 High
