CMP3005 Analysis of AlgorithmsBahçeşehir UniversityDegree Programs PSYCHOLOGYGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
PSYCHOLOGY
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: 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.

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

Sources

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
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
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 develop an interest in the human mind and behavior, to be able to evaluate theories using empirical findings, to understand that psychology is an evidence-based science by acquiring critical thinking skills.
2) To gain a biopsychosocial perspective on human behavior. To understand the biological, psychological, and social variables of behavior.
3) To learn the basic concepts in psychology and the theoretical and practical approaches used to study them (e.g. basic observation and interview techniques).
4) To acquire the methods and skills to access and write information using English as the dominant language in the psychological literature, to recognize and apply scientific research and data evaluation techniques (e.g. correlational, experimental, cross-sectional and longitudinal studies, case studies).
5) To be against discrimination and prejudice; to have ethical concerns while working in research and practice areas.
6) To recognize the main subfields of psychology (experimental, developmental, clinical, cognitive, social and industrial/organizational psychology) and their related fields of study and specialization.
7) To acquire the skills necessary for analyzing, interpreting and presenting the findings as well as problem posing, hypothesizing and data collection, which are the basic elements of scientific studies.
8) To gain the basic knowledge and skills necessary for psychological assessment and evaluation.
9) To acquire basic knowledge of other disciplines (medicine, genetics, biology, economics, sociology, political science, communication, philosophy, anthropology, literature, law, art, etc.) that will contribute to psychology and to use this knowledge in the understanding and interpretation of psychological processes.
10) To develop sensitivity towards social problems; to take responsibility in activities that benefit the field of psychology and society.
11) To have problem solving skills and to be able to develop the necessary analytical approaches for this.
12) To be able to criticize any subject in business and academic life and to be able to express their thoughts.