Ders Planı

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CLASS 3

Lesson plan / ALGORITHM ANALYSIS

Lesson Information

Course Credit	3.0
Course ECTS Credit	5.0
Teaching Language of Instruction	Türkçe
Level of Course	Bachelor's Degree, TYYÇ: Level 6, EQF-LLL: Level 6, QF-EHEA: First Cycle
Type of Course	Compulsory
Mode of Delivery	Face-to-face
Does the course require compulsory or optional work experience?	Z
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Purpose and Content

The aim of the course	Students will be able to analyse the structures of algorithms, their design and application problems, complexity of algorithms and computation problems, types of algorithms and runtime estimation methods. All these are necessary to design and apply workable programs to solve the practical problems.
Course Content	The course will introduce the concept of algorithm, Simplist sorting algorithms, Designing approachs: recursive and divide-and-conquer strategies; asymptotic notations, complexity analysis, running times defining, algorithm design techniques: greedy methods, min-cost spanning trees,backtracking algorithms, branch-and-bound methodolog

Learning Outcomes

Has theoretical knowledge in the field of statistics.
Has the ability to use the theoretical resources required for statistics
Has the ability to use the theoretical resources required for statistics
To be able to use the necessary theoretical content resources for statistics
Defines the studies related to statistics independently and with the partners
Describe the current problems with statistical methods
Defines the ability of abstract and analytical thinking
Comprehend the knowledge of computer software and programming technique to be able to apply the science of statistics effectively.
Analyze the theoretical knowledge in probability and statistics
Evaluate different databases as required
Evaluates the outputs of applied questionnaires
Applies the concepts and thoughts in the field with scientific methods
Arrange the data related to the field
Solve the identified statistical problems
Prepares surveys
Develops solutions based on identified statistical problems, evidence and research
Contributes to the professional development of the employees under their management
Define new topics related to statistics
Give lectures and seminars about statistics and probability
Determines the needs for learning
Develops original attitudes towards lifelong learning by guiding learning processes related to learning
He/She organizes projects and activities for the social environment in which he/she lives with an awareness of social responsibility
Students will be able to communicate their solutions to problems related to statistics in written and oral forms
Supports suggestions for solutions and thoughts with quantative and quantative data
Follow developments in the field by using a foreign language
Uses computer software and information-communication-network technologies at the level required by the science of statistics
Students will have social, scientific and ethical values in the publication of the data related to the fields of science
Quality management and process, environmental protection, occupational health and safety issues has sufficient awareness
Gain social, scientific and ethical values in the stages of collecting, interpreting and announcing the data related to the fields of science
Has knowledge about the legal consequences of statistical applications

Weekly Course Subjects

1	Introduction. Insertion sort algorithm.
2	Algorithm analysing methods.Examples
3	Designing algorithms: recursive and divide-and-conquer methods.
4	Asymptotic notations.
5	Practical complexity analysis.
6	Running times defining.
7	Midterm exam.
8	Linear lists: a chain iterator class in C++.
9	Arrays as classes.
10	Trees: tournament and binary-search trees.
11	AVL and B-trees.
12	Algorithm-design techniques: the Greedy method, Divide-and-conquer method.
13	Backtracking algorithms. Branch-and-Bound methodology
14	Final exam.

Resources

Cormen T.H. etc. "Introduction to Algorithms", The MIT Press, 2001.

CLASS 1

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CLASS 3

CLASS 4

Lesson Information

Purpose and Content

Learning Outcomes

Weekly Course Subjects

Resources

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