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| ISTL7062 | Machine Learning for Big Data | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Uğur ŞEVİK | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The history of Big Data, data sources, data types, the structure and characteristics of big data, current technologies, tools, architectures and systems, covering analytical data production, storage, management, transfer and in-depth analysis of incoming big data. Also understanding of phenomena such as artificial intelligence, machine learning algorithms and deep learning to analyze big data. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | They will learn big data concepts, terminology, data analytics features, big data types such as 3V, 5V, 10V-structural-unstructured-metadata. | 1,2,3,8 | 3, | | PO - 2 : | They comprehend analysis techniques such as qualitative and quantitative data mining, statistical analysis, A/B testing, correlation, regression analysis. | 1,2,3,8 | 3, | | PO - 3 : | They use machine learning methods on big data. | 1,2,3,8 | 4, | | PO - 4 : | They master storage concepts such as clustering, distributed file systems, relational database systems, NoSQL, in-memory storage, and big data processing concepts such as parallel, distributed, batch data processing. | 1,2,3,8 | 3, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Data mining, knowledge discovery, preprocessing, classification methods, clustering methods, association rules and model evaluation approaches used for big data analysis will be examined. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Big data concept and philosophy | | | Week 2 | Big data analysis and management tools | | | Week 3 | Big data and machine learning approaches | | | Week 4 | Big data and machine learning approaches | | | Week 5 | Big data and machine learning approaches | | | Week 6 | Models attribute selection and model creation | | | Week 7 | Models attribute selection and model creation | | | Week 8 | | | | Week 9 | Homework Presentation | | | Week 10 | Machine learning applications on big data | | | Week 11 | Machine learning applications on big data | | | Week 12 | Homework Presentation | | | Week 13 | Machine learning applications on big data | | | Week 14 | Machine learning applications on big data | | | Week 15 | Machine learning applications on big data | | | Week 16 | | | | |
| 1 | Suthaharan S., 2015, Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, Springer. | | | |
| 1 | Dean J., 2014, Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners, Wiley. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Homework/Assignment/Term-paper | 9
12 | 22/11/2021 | 2 | 50 | | End-of-term exam | 17 | 17/01/2022 | 3 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 16 | 48 | | Sınıf dışı çalışma | 1 | 16 | 16 | | Ödev | 2 | 2 | 4 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 70 |
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