Graph Theory(VM 524)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| VM 524 | Graph Theory | 3 | 4 | 0 | 0 | 3 | 8 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | |
| Course Level | Masters Degree |
| Course Instructor(s) | Serap GÜRER serapgurer@gmail.com (Email) |
| Assistant | |
| Objective | This course introduces the fundamental principles of graph theory and explores its applications in data science. Students will learn how to represent, analyze, and manipulate various types of graphs to solve real-world problems in data analysis, network science, and machine learning. |
| Content |
Fundamental Graph Theory Concepts: Paths and cycles, connectivity, trees, spanning subgraphs, bipartite graphs, Hamiltonian and Euler cycles. Graph Algorithms. Network Analysis. GCN (Graph Convolutional Networks). Data Science Applications. |
| Course Learning Outcomes | |
| Teaching and Learning Methods | |
| References |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Introduction to Graphs |
| 2 | Graph Algorithms |
| 3 | Graph Properties and Metrics |
| 4 | Graph Visualization |
| 5 | Social Network Analysis |
| 6 | Recommender Systems |
| 7 | Midterm |
| 8 | Graphs in Machine Learning |
| 9 | Graphs in Machine Learning |
| 10 | Web and Text Mining |
| 11 | Advanced Topics |
Practice Topics
| Week | Weekly Contents |
|---|---|
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| 5 | |
| 6 | |
| 7 | |
| 8 | |
| 9 | |
| 10 | |
| 11 |
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Toplam | 0 | 0 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Toplam | 0 | 0 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Total Workload | 0 | ||
| Total Workload / 25 | 0,00 | ||
| Credits ECTS | 0 | ||