ECONOMICS | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP3005 | Analysis of Algorithms | Fall | 3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
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 |
Recommended Optional Program Components: | None |
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 / Textbooks: | 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 |
Quizzes | 2 | % 10 |
Project | 1 | % 20 |
Midterms | 1 | % 30 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
Total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 14 | 42 |
Project | 7 | 21 |
Quizzes | 6 | 12 |
Midterms | 5 | 28 |
Final | 5 | 35 |
Total Workload | 138 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | As a world citizen, she is aware of global economic, political, social and ecological developments and trends. | 2 |
2) | He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning. | 2 |
3) | Absorbs basic economic principles and analysis methods and uses them to evaluate daily events. | 2 |
4) | Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders. | 2 |
5) | Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions. | 1 |
6) | Comprehends new ways of doing business using digital technologies. and new market structures. | 2 |
7) | Takes critical approach to economic and social problems and develops analytical solutions. | 1 |
8) | Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods. | 2 |
9) | In the works he/she contributes, observes individual and social welfare together and with an ethical perspective. | 2 |
10) | Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines. | 1 |
11) | Generates original and innovative ideas in the works she/he contributes as part of a team. | 2 |