| School/Faculty/Institute |
Graduate School |
| Course Code |
CSE 602 |
| Course Title in English |
Advanced Data Science and Engineering |
| Course Title in Turkish |
İleri Veri Bilimi ve Mühendisliği |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Advanced |
| Semester |
Spring |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
|
| Estimated Student Workload |
188 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
None |
| Expected Prior Knowledge |
None |
| Co-requisites |
None |
| Registration Restrictions |
Only Doctorate Students |
| Overall Educational Objective |
To learn advanced data science and engineering methods and how to construct and implement intelligent systems on the big data set by new hybrid models. |
| Course Description |
This course is mainly designed to provide common advanced techniques in data science and engineering. This graduate course provides a comprehensive approach to big data analytics, exploratory data analysis, and enriched visualization, advanced machine learning as well as deep learning. The course content has a special emphasis on the mathematical foundations related to the commonly used algorithms in artificial intelligence, machine learning as well as deep learning. There will also be cases on practical applications including architectural decisions, applying the relevant algorithms, selecting suitable evaluative metrics for increasing model performance, and scalability. |
| Course Description in Turkish |
Bu ders temel olarak veri bilimi ve mühendisliğinde yaygın olarak kullanılan ileri metotları öğretmek için tasarlanmıştır. Bu ders, büyük veri analitiği, keşifsel veri analizi ve zenginleştirilmiş görselleştirme, ileri yapay öğrenme ve derin öğrenmeye kapsamlı bir yaklaşım sağlar. Ders içeriği, yapay zekanın farklı alt dallarında yaygın olarak kullanılan algoritmalarla ilgili matematiksel temellere özel bir vurgu yapmaktadır. Ayrıca mimari kararlar, ilgili algoritmaları uygulama, model performansını artırmak için uygun değerlendirme ölçütlerini seçme ve ölçeklenebilirlik dahil olmak üzere pratik uygulamalara vakalar üzerinden giriş sağlanacaktır. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Apply big data-based machine learning and AI algorithms to investigate patterns in data set
2) Apply advanced classification and clustering methods to predict future trend in big data
3) Assess the performance of the data science methods for big data
4) Design and construct hybrid data processing methods for big data
5) Communicate effectively by means of reports and presentations
6) Analyze and interpret data, and use analytic thinking to draw conclusions
7) Acquire and apply new knowledge as needed
|
| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
|
|
| |
Program Outcomes and Competences |
Level |
Assessed by |
| Prepared by and Date |
, |
| Course Coordinator |
İLKER BEKMEZCİ |
| Semester |
Spring |
| Name of Instructor |
Prof. Dr. İLKER BEKMEZCİ |
Course Contents
| Week |
Subject |
| 1) |
Big data pre-processing and quality considerations |
| 2) |
Computational complexity of data-intensive computing |
| 3) |
Innovative methods for the design of the algorithms for solving big data problems |
| 4) |
Representation, modeling, and visualization of big data |
| 5) |
Storage, and management of big data |
| 6) |
Data Mining methods and approaches for big data |
| 7) |
Big databased machine learning |
| 8) |
Big databased decision making |
| 9) |
Statistical computation of big data |
| 10) |
Graph-theoretic computation of big data |
| 11) |
Optimization of big data |
| 12) |
Novel applications in big data |
| 13) |
Novel hybrid methods for big data |
| 14) |
New approaches and new trends for big data |
| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
Information Package / Course Catalogue