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
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

To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems.
2

To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment.
3

To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality.
4

To be able to conduct basic research and write and publish articles in related conferences and journals.
5

To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results.
6

To be able to manage decision-making processes in industrial systems.
7

To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary.
8

To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering.
9

To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria.
10

To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.

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