ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP3005 Analysis of Algorithms Fall 3 0 3 6
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Must Course
Course Level: Bachelor
Mode of Delivery: Face to face
Course Coordinator : Assist. Prof. GÜNET EROĞLU
Course Lecturer(s): Assoc. Prof. TEVFİK AYTEKİN
Prof. Dr. NAFİZ ARICA
Assoc. Prof. CEMAL OKAN ŞAKAR
Course Objectives: The objective of the course is to introduce the fundamental mathematical tools needed to analyze algorithms, basic algorithm design techniques, advanced data structures, and important algorithms from different problem domains.

Learning Outputs

The students who have succeeded in this course;
I. Become familiar with some major advanced data structures and algorithms.
II. Become familiar with mathematical tools used in analyzing algorithms.
III. Be able to analyze the asymptotic running time of an (iterative/recursive) algorithm.
IV. Be able to make best/worst/average case analysis of algorithms.
V. Become familiar with important algorithm design paradigms.
VI. Be able to decide which data structure/algorithm among a set of possible choices is best for a particular application.
VII. Be able to recognize and distinguish efficient and inefficient algorithms.
VIII. Be able to design efficient algorithms for new problems using the techniques learned and apply/report these solutions in an intra-discipline project group.

Course Content

Introduction, asymptotic notation, empirical analysis of algorithms, designing algorithms, amortized analysis, brute force algorithms, divide and conquer algorithms, transform and conquer algorithms, space and time trade-offs, dynamic programming, greedy algorithms, advanced data structures, B-trees, Insertion and Deletion from B-trees, graphs and graph algorithms, P, NP, and NP-complete problems.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction, asymptotic notation.
2) Empirical analysis of algorithms, analysis of algorithms, amortized analysis
3) Recurrences, substitution method, recursion-tree method, master method.
4) Brute Force Algorithms
5) Divide and Conquer Algorithms
6) Merge sort, quicksort, randomized quicksort, binary search
7) Transform and Conquer Algorithms: Solving systems of linear equations with Gaussian ination elimination, Balanced Search Trees, Heaps and Heapsort, Horner's Rule and Binary Exponentiation
8) Space and Time Trade-offs: Input Enhancement (Counting based sorting, string matching), Prestructuring (Hashing, Hash functions, open addressing).
9) Midterm
10) Dynamic Programming: Coin-row problem, Knapsack problem, Longest common subsequence.
11) Dynamic Programming: Knapsack problem, Longest common subsequence.
12) Greedy Algorithms: Activity selection, Huffman codes, Prim’s algorithm, Kruskal’s Algorithm
13) Single-source shortest paths: The Bellman-Ford algorithm, Dijkstra's algorithm.
14) P, NP, and NP-complete problems

Sources

Course Notes: Anany Levitin, The Design and Analysis of Algorithms, Pearson International Third Edition. Cormen, T. H., Leiserson, C. E., Rivest, R. L. and Stein, C., Introduction to Algorithms (3rd Edition), MIT Press, 2009. Sanjoy Dasgupta , Christos Papadimitriou, Umesh Vazirani, Algorithms, McGraw-Hill Education.
References: Yok - None

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 0 % 0
Laboratory 0 % 0
Application 0 % 0
Field Work 0 % 0
Special Course Internship (Work Placement) 0 % 0
Quizzes 2 % 10
Homework Assignments 0 % 0
Presentation 0 % 0
Project 1 % 20
Seminar 0 % 0
Midterms 1 % 30
Preliminary Jury 0 % 0
Final 1 % 40
Paper Submission 0 % 0
Jury 0 % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Laboratory
Application
Special Course Internship (Work Placement)
Field Work
Study Hours Out of Class
Presentations / Seminar
Project 7 21
Homework Assignments
Quizzes 6 12
Preliminary Jury
Midterms 5 28
Paper Submission
Jury
Final 5 35
Total Workload 138

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Build up a body of knowledge in mathematics, science and Artificial Intelligence Engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems. 5
2) Design complex Artificial Intelligence systems, platforms, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. 5
3) Identify, formulate, and solve complex Artificial Intelligence Engineering problems; select and apply proper modeling and analysis methods for this purpose. 3
4) Devise, select, and use modern techniques and tools needed for solving complex problems in Artificial Intelligence Engineering practice; employ information technologies effectively. 3
5) Design and conduct numerical or physical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Artificial Intelligence Engineering. 4
6) Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. Write and understand reports, prepare design and production reports, deliver effective presentations, give and receive clear and understandable instructions. 4
7) Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself. 4
8) Develop an awareness of professional and ethical responsibility, and behave accordingly. Be informed about the standards used in Artificial Intelligence Engineering applications. 3
9) Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
10) Acquire knowledge about the effects of practices of Artificial Intelligence Engineering on health, environment, security in universal and social scope, and the contemporary problems of Artificial Intelligence Engineering; is aware of the legal consequences of Mechatronics engineering solutions.
11) Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Artificial Intelligence-related problems.