COURSE INTRODUCTION AND APPLICATION INFORMATION


Course Name
Advanced Algorithms
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 601
Fall/Spring
3
0
3
7.5
Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Third Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives The objective of this course is to provide a comprehensive and detailed study of the design and analysis of algorithms. The course aims at discussing state of the art algorithms and related data structures that are crucial to achieve satisfactory levels of performance in industry and research since the processor speed-ups we have been enjoying are coming to an end and better algorithms are needed in order to cope with the increasing demand of data processing.
Learning Outcomes The students who succeeded in this course;
  • will be able to apply the Master Theorem in order to analyze the time and space complexity of algorithms.
  • will be able to employ randomized algorithms.
  • will be able to design and apply advanced data structures for solving computing problems.
  • will be able to design efficient algorithms for solving computing problems.
  • will be able to have an increased number of solutions in their portfolio of algorithms to tackle an even more diverse set of problems.
  • will be able to solve optimization problems by dynamic programming methods.
  • will be able to assess the trade-off between time and optimality and use approximation algorithms when the optimal is not feasible.
Course Description The course covers master theorem for solving recurrences, probabilistic analysis, amortized analysis, greedy algorithms, divide-and-conquer type of algorithms, dynamic programming and approximation algorithms.
Related Sustainable Development Goals

 



Course Category

Core Courses
X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 The Role of Algorithms in Computing, Growth of Functions T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 1, 2, 3)
2 Recurrences, Master Theorem T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 4)
3 Probabilistic Analysis and Randomized Algorithms T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 5)
4 Medians and Order Statistics T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 9)
5 Red-Black Trees T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 13)
6 Augmenting Data Structures T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 14)
7 Dynamic Programming T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 15)
8 Greedy Algorithms T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 16)
9 Amortized Analysis T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 17)
10 Number-Theoretic Algorithms T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 31)
11 String Matching T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 32)
12 NP-Completeness T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 34)
13 Approximation Algorithms T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3/e, The MIT Press, 2009 (Ch. 35)
14 Learning Algorithms, Decision Tree Learning
15 Review of the semester
16 -
Course Notes/Textbooks The textbook referenced above and course slides
Suggested Readings/Materials Related Research Papers

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterm
2
50
Final Exam
1
50
Total

Weighting of Semester Activities on the Final Grade
50
Weighting of End-of-Semester Activities on the Final Grade
50
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
Study Hours Out of Class
15
6
90
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
2
25
Final Exams
1
37
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Understands and applies the foundational theories of Computer Engineering in a high level. X
2 Possesses a great depth and breadth of knowledge about Computer Engineering including the latest developments. X
3 Can reach the latest information in Computer Engineering and possesses a high level of proficiency in the methods and abilities necessary to comprehend it and conduct research with it. X
4 Conducts a comprehensive study that introduces innovation to science and technology, develops a new scientific procedure or a technological product/process, or applies a known method in a new field.  X
5 Independently understands, designs, implements and concludes a unique research process in addition to managing it.  X
6 Contributes to science and technology literature by publishing the output of his/her academic studies in respectable academic outlets. X
7 Interprets scientific, technological, social and cultural developments and relates them to the general public with a commitment to scientific objectivity and ethical responsibility. X
8 Performs critical analysis, synthesis and evaluation of ideas and developments in Computer Engineering. X
9 Performs verbal and written communications with professionals as well as broader scientific and social communities in Computer Engineering, by using English at least at the European Language Portfolio C1 General level, performs written, oral and visual communications and discussions in a high level. X
10 Develops strategies, policies and plans about systems and topics that Computer Engineering uses, and interprets the outcomes. X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest