TR
11111

COURSE INTRODUCTION AND APPLICATION INFORMATION

dm.ieu.edu.tr

Course Name
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
Fall/Spring
Prerequisites
None
Course Language
Course Type
Elective
Course Level
-
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Course Coordinator -
Course Lecturer(s) -
Assistant(s) -
Course Objectives
Learning Outcomes The students who succeeded in this course;
  • Learn how to isolate the underlying structure of the problem to tackle it.
  • Analyze the time and space complexity of algorithms.
  • Have an increased number of solutions in their portfolio of algorithms to tackle an even more diverse set of problems.
  • Efficiently solve problems with greedy algorithms that can be modeled directly or through a sequence of transformations as instances of interval scheduling, interval partitioning, scheduling to minimize lateness, clustering, and minimum-cost arborescence problems.
  • Understand whether a problem can be solved with the help of a divide-and-conquer algorithm and efficiently solve problems that can be modeled directly or through a sequence of transformations as instances of inversion counting, closest pair of points and integer multiplication and use fast Fourier transformation for developing efficient algorithms.
  • Learn how to model dynamic programming solutions for weighted interval scheduling, segmented least squares and sequence alignment problems and capitalize upon this to generalize it.
  • Assess the trade-off between time and optimality and use approximation algorithms when the optimal is not feasible and distinguish those problems to which load balancing and set cover can be reduced They will use the approximation bounds obtained for load balancing and set cover for similar intractable problems where possible.
Course Description

 



Course Category

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

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Introduction and motivation. Mathematical foundations, summation, recurrences and growth of functions Cormen Chapter 2, 3, and 4
2 Asymptotic notation and Master theorem Cormen Chapter 4
3 Binary heaps and analysis of heapsort Cormen Chapter 6
4 Sorting theory and more comparison sorting algorithms: Analysis of merge sort andQuicksort. Cormen Chapter 7
5 Worst case analysis of Quicksort Cormen Chapter 7
6 Sorting in linear time, lower bounds for sorting, counting sort, radix sort, bucket sort Cormen Chapter 8
7 Medians and order statistics. Finding median and rank in linear time, selectionalgorithm. Cormen Chapter 9
8 Midterm
9 Elementary data structures and runtime analysis of insertion, deletion and update Cormen Chapter 10
10 Hash tables and runtime analysis. Cormen Chapter 11
11 Binary search trees and Redblack trees Cormen Chapter 12 and 13
12 Btrees and Augmenting data structures Cormen Chapter 18
13 Amortized analysis Cormen Chapter 17
14 Binomial heaps and fibonacci heaps Cormen Chapter 19 and 20
15 Review
16 Review of the Semester  
Course Notes/Textbooks Introduction to Algorithms, 2/eThomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, ISBN: 9780262533058, MIT PressData Structures and Algorithm Analysis in C++, Mark Allen Weiss, Addision Wesley, Third Edition.
Suggested Readings/Materials Algorithm Design. Jon Kleinberg and Eva Tardos. 2006, Pearson Education, ISBN 0321372913

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
6
30
Seminar / Workshop
Oral Exam
Midterm
1
30
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
60
Weighting of End-of-Semester Activities on the Final Grade
40
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
2
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
6
2
Seminar / Workshop
Oral Exam
Midterms
1
10
Final Exams
1
20
    Total
120

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
 Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 To have a grasp of basic mathematics, applied mathematics and theories and applications of statistics.
2 To be able to use theoretical and applied knowledge acquired in the advanced fields of mathematics and statistics,
3 To be able to define and analyze problems and to find solutions based on scientific methods,
4 To be able to apply mathematics and statistics in real life with interdisciplinary approach and to discover their potentials, X
5 To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself, X
6 To be able to criticize and renew her/his own models and solutions,
7 To be able to tell theoretical and technical information easily to both experts in detail and nonexperts in basic and comprehensible way,
8

To be able to use international resources in English and in a second foreign language from the European Language Portfolio (at the level of B1) effectively and to keep knowledge up-to-date, to communicate comfortably with colleagues from Turkey and other countries, to follow periodic literature,
9

To be familiar with computer programs used in the fields of mathematics and statistics and to be able to use at least one of them effectively at the European Computer Driving Licence Advanced Level,

 X
10

To be able to behave in accordance with social, scientific and ethical values in each step of the projects involved and to be able to introduce and apply projects in terms of civic engagement,
11 To be able to evaluate all processes effectively and to have enough awareness about quality management by being conscious and having intellectual background in the universal sense,
12

By having a way of abstract thinking, to be able to connect concrete events and to transfer solutions, to be able to design experiments, collect data, and analyze results by scientific methods and to interfere,
13

To be able to continue lifelong learning by renewing the knowledge, the abilities and the compentencies which have been developed during the program, and being conscious about lifelong learning,
14

To be able to adapt and transfer the knowledge gained in the areas of mathematics and statistics to the level of secondary school,
15

To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively.

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

 

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