COMPUTER ENGINEERING | |||||
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 |
Language of instruction: | English |
Type of course: | Must Course |
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) | Adequate knowledge in mathematics, science and computer engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems. | 5 |
2) | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 5 |
3) | Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | 3 |
4) | Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; ability to use information technologies effectively. | 3 |
5) | Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or computer engineering research topics. | 4 |
6) | Ability to work effectively within and multi-disciplinary teams; individual study skills. | 4 |
7) | Ability to communicate effectively in verbal and written Turkish; knowledge of at least one foreign language; ability to write active reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. | 4 |
8) | Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously. | 3 |
9) | To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications. | 3 |
10) | Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development. | 3 |
11) | Knowledge of the effects of engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in engineering; awareness of the legal consequences of engineering solutions. |