| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP3005 | Analysis of Algorithms | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
| Course Lecturer(s): |
Dr. Öğr. Üyesi TEVFİK AYTEKİN Prof. Dr. NAFİZ ARICA Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
| Course Objectives: | The objective of the course is to introduce the fundamental mathematical tools needed to analyze algorithms, basic algorithm design techniques, advanced data structures, and important algorithms from different problem domains. |
|
The students who have succeeded in this course; I. Become familiar with some major advanced data structures and algorithms. II. Become familiar with mathematical tools used in analyzing algorithms. III. Be able to analyze the asymptotic running time of an (iterative/recursive) algorithm. IV. Be able to make best/worst/average case analysis of algorithms. V. Become familiar with important algorithm design paradigms. VI. Be able to decide which data structure/algorithm among a set of possible choices is best for a particular application. VII. Be able to recognize and distinguish efficient and inefficient algorithms. VIII. Be able to design efficient algorithms for new problems using the techniques learned and apply/report these solutions in an intra-discipline project group. |
| Introduction, asymptotic notation, empirical analysis of algorithms, designing algorithms, amortized analysis, brute force algorithms, divide and conquer algorithms, transform and conquer algorithms, space and time trade-offs, dynamic programming, greedy algorithms, advanced data structures, B-trees, Insertion and Deletion from B-trees, graphs and graph algorithms, P, NP, and NP-complete problems. |
| Week | Subject | Related Preparation | |
| 1) | Introduction, asymptotic notation. | ||
| 2) | Empirical analysis of algorithms, analysis of algorithms, amortized analysis | ||
| 3) | Recurrences, substitution method, recursion-tree method, master method. | ||
| 4) | Brute Force Algorithms | ||
| 5) | Divide and Conquer Algorithms | ||
| 6) | Merge sort, quicksort, randomized quicksort, binary search | ||
| 7) | Transform and Conquer Algorithms: Solving systems of linear equations with Gaussian ination elimination, Balanced Search Trees, Heaps and Heapsort, Horner's Rule and Binary Exponentiation | ||
| 8) | Space and Time Trade-offs: Input Enhancement (Counting based sorting, string matching), Prestructuring (Hashing, Hash functions, open addressing). | ||
| 9) | Midterm | ||
| 10) | Dynamic Programming: Coin-row problem, Knapsack problem, Longest common subsequence. | ||
| 11) | Dynamic Programming: Knapsack problem, Longest common subsequence. | ||
| 12) | Greedy Algorithms: Activity selection, Huffman codes, Prim’s algorithm, Kruskal’s Algorithm | ||
| 13) | Single-source shortest paths: The Bellman-Ford algorithm, Dijkstra's algorithm. | ||
| 14) | P, NP, and NP-complete problems | ||
| Course Notes: | Anany Levitin, The Design and Analysis of Algorithms, Pearson International Third Edition. Cormen, T. H., Leiserson, C. E., Rivest, R. L. and Stein, C., Introduction to Algorithms (3rd Edition), MIT Press, 2009. Sanjoy Dasgupta , Christos Papadimitriou, Umesh Vazirani, Algorithms, McGraw-Hill Education. |
| References: | Yok - None |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 0 | % 0 |
| Laboratory | 0 | % 0 |
| Application | 0 | % 0 |
| Field Work | 0 | % 0 |
| Special Course Internship (Work Placement) | 0 | % 0 |
| Quizzes | 2 | % 10 |
| Homework Assignments | 0 | % 0 |
| Presentation | 0 | % 0 |
| Project | 1 | % 20 |
| Seminar | 0 | % 0 |
| Midterms | 1 | % 30 |
| Preliminary Jury | 0 | % 0 |
| Final | 1 | % 40 |
| Paper Submission | 0 | % 0 |
| Jury | 0 | % 0 |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | |||
| Presentations / Seminar | |||
| Project | 7 | 21 | |
| Homework Assignments | |||
| Quizzes | 6 | 12 | |
| Preliminary Jury | |||
| Midterms | 5 | 28 | |
| Paper Submission | |||
| Jury | |||
| Final | 5 | 35 | |
| Total Workload | 138 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |