CMP3005 Analysis of AlgorithmsBahçeşehir UniversityDegree Programs NUTRITION AND DIETETIC (ENGLISH)General Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP3005 Analysis of Algorithms Spring 3 0 3 6
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of 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
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.

Learning Outcomes

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.

Course Content

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.

Weekly Detailed Course Contents

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

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Quizzes 2 % 10
Project 1 % 20
Midterms 1 % 30
Final 1 % 40
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Project 7 21
Quizzes 6 12
Midterms 5 28
Final 5 35
Total Workload 138

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution
1) To use theoretic and methodological approach, evidence-based principles and scientific literature in Nutrition and Dietetics field systematically for practice.
2) To have theoretic and practical knowledge for individual's, family's and the community's health promotion and protection.
3) To assess nutritional status of risky groups in nutrition related problems (pregnant, babies, adolescences, elders, etc.)
4) To use healthcare, information technologies for Nutrition and Dietetic practice and research.
5) To communicate effectively with advisee, colleagues for effective professional relationships.
6) To be able to monitor occupational information using at least one foreign language, to collaborate and communicate with colleagues at international level.
7) To use life-long learning, problem-solving and critical thinking skills.
8) To act in accordance with ethical principles and values in professional practice.
9) To take part in research, projects and activities within sense of social responsibility and interdisciplinary approach.
10) To be able to search for literature in health sciences databases and information sources to access to information and use the information effectively.
11) To take responsibility and participate in the processes actively for training of other dieticians, education of health professionals and individuals about nutrition.
12) To carry out dietetic practices considering cultural differences and different health needs of different groups in the community.