COURSE UNIT TITLE

: ANALYSIS OF ALGORITHMS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5083 ANALYSIS OF ALGORITHMS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ZERRIN IŞIK

Offered to

Computer Engineering Non-Thesis
COMPUTER ENGINEERING
Computer Science
Computer Engineering
Ph.D. in Computer Science
Computer Engineering
Computer Engineering (Non-Thesis-Evening)

Course Objective

This course aims at equipping students with knowledge and skills to identify, analyze and asess alternatives of more advanced algorithms and data structures in developing solutions to modern engineering problems. At the end of the course students should be able to recognize limits on performance of algorithms and be able to develop practically acceptable solutions to some harder engineering problems using approximation and probability methods.

Learning Outcomes of the Course Unit

1   Define, implement, analyze and apply to engineering problems B-Trees, Fibonacci Heaps, van Emde Boas Trees, Disjoint Sets,
2   Define, implement, analyze and apply to engineering problems All-Pairs Shortest Path ve Maximum Flow Algorithms,
3   Define, implement, analyze and apply to engineering problems Multithreaded Algorithms, Linear Programming and Some Matrix Algorithms
4   Define, implement, analyze and apply to engineering problems Polynomial and FFT Algorithms,
5   Define performance limits of Algorithms, design and develop Approximation Algorithms, Probabilistic Algorithms to hard problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Review of Algorithm Analysis Methods, Probabaility, Indicator Random Variables, Recurrence Equations
2 Advanced Data Structures: B-Trees
3 Advanced Data Structures: Fibonacci Heaps
4 Advanced Data Structures: van Emde Boas Trees
5 Advanced Data Structures: Data Structures for Disjoint Sets
6 Advanced Graph Algorithms: All-Pairs Shortest Paths
7 Midterm Exam Review
8 Advanced Graph Algorithms: Maximum Flow
9 Multithreaded Algorithms
10 Matrix Operations
11 Linear Programming
12 Polynomials and the FFT
13 NP-Completeness
14 Approximation Algorithms

Recomended or Required Reading

Textbook(s): Introduction To Algorithms, Third Edition, THOMAS H. CORMEN CHARLES E. LEISERSON RONALD L. RIVEST CLIFFORD STEIN, The MIT Press Massachusetts Institute of Technology Cambridge, 2009.
Supplementary Book(s):
References:
Materials: Lecture Notes,problem sets.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 +ASG * 0.20 +FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Prof. Dr. Suleyman Sevinc
suleyman.sevinc@gmail.com
Tel: 0232 301 7403 / 7401

Office Hours

TBA in the first lecture

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 39 1 39
Preparations before/after weekly lectures 13 5 65
Preparation for midterm exam 2 10 20
Preparation for final exam 1 15 15
Preparation for quiz etc. 3 3 9
Preparing assignments 4 4 16
Reading 3 7 21
Final 1 2 2
Midterm 2 2 4
Quiz etc. 3 3 9
TOTAL WORKLOAD (hours) 200

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1545
LO.2545
LO.3545
LO.4545
LO.5545

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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