COURSE INFORMATION Course Title: RANDOMIZED ALGORITHMS

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 879	C	99	3	2	0	4	7.5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Elton Domnori
Second Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Classroom and Meeting Time:
Course Description:	-
Course Objectives:	The course has two main themes. The first theme is basic tools from probabilistic analysis that are recurrent in algorithmic applications. The second theme is specific areas of application. Tools will be interleaved with applications that illustrate them in concrete settings. This course aims to confer: (1) some familiarity with several of the main thrusts of work in randomized algorithms—giving you context for formulating and seeking known solutions to an algorithmic problem; (2) background and facility to read current research publications in the area of algorithms; and (3) a set of tools for design and analysis of new algorithms for new problems that you encounter.

COURSE OUTLINE

Week	Topics
1	Introduction and administrivia
2	Game tree evaluation. The minimax principle. Randomness and non-uniformity.
3	Moments and deviations.
4	Tail inequalities
5	The probabilistic method
6	Markov chains and random walks
7	Data structures
8	Midterm
9	Geometric algorithms
10	Graph algorithms
11	Approximate counting
12	Online algorithms
13	Information theory
14	Course overview

Prerequisite(s):
Textbook:	Motwani and Raghavan, Randomized Algorithms, Cambridge University Press, 1995.
Other References:
Laboratory Work:	Yes
Computer Usage:	Yes
Others:	No

COURSE LEARNING OUTCOMES

1	Analyze and develop an ability to develop and implement appropriate algorithms
2	Understand the strengths and requirements of probabilistic approach

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Doctorate (PhD) in Computer Engineering Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.
8	Ability for effective oral and official communication skills in foreign language.
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.
10	Engineering graduates with well-structured responsibilities in profession and ethics.
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	40
Quiz	2	10
Final Exam	1	40
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	5	80
Hours for off-the-classroom study (Pre-study, practice)	16	5	80
Mid-terms	1	12	12
Assignments			0
Final examination	1	15.5	15.5
Other			0
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5