Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty Of Computer And Information Technologies
  • Artificial Intelligence And Machine Learning Pr. (English)
  • Course Structure Diagram with Credits
  • Algorithm Design and Analysis
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
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  • ECTS Credit Load

Course Introduction Information
Code - Course Title YZM2002 - Algorithm Design and Analysis
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 5.0
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery This course is offered exclusively in an in-person format.
Prerequisites There are no prerequisites or co-requisites for this course.
Courses Recomended There are no other recommended courses related to this one.
Required or Recommended Resources Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms (4th ed.). MIT Press. Kleinberg, J., & Tardos, É. (2005). Algorithm design. Pearson.
Recommended Reading List Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms (4th ed.). MIT Press. Kleinberg, J., & Tardos, É. (2005). Algorithm design. Pearson.
Assessment methods and criteria Midterm, Project, and Final Exam
Work Placement This is not suitable for this class.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 What is an Algorithm? Review of Complexity Analysis (O, Ω, Θ), Best/Average/Worst Case.
Week - 2 Divide and Conquer I: Analysis of Merge Sort & Quick Sort. Recurrence Relations.
Week - 3 Divide and Conquer II: The Master Theorem and its applications.
Week - 4 Greedy Algorithms I: Concept, Activity Selection Problem.
Week - 5 Greedy Algorithms II: Fractional Knapsack Problem, Huffman Codes.
Week - 6 Greedy Algorithms III: Minimum Spanning Trees (MST) - Kruskal's Algorithm.
Week - 7 Greedy Algorithms IV: Minimum Spanning Trees (MST) - Prim's Algorithm.
Week - 8 Graph Algorithms: Shortest Path I - Dijkstra's Algorithm.
Week - 9 Graph Algorithms: Shortest Path II - Bellman-Ford Algorithm (Negative weights).
Week - 10 Dynamic Programming I: Concept (Optimal Substructure, Overlapping Subproblems).
Week - 11 Dynamic Programming II: Memoization vs. Tabulation. Knapsack Problem.
Week - 12 Dynamic Programming III: Longest Common Subsequence (LCS).
Week - 13 String Matching: Naive Approach, KMP Algorithm.
Week - 14 Theory of NP-Completeness: P, NP, NP-Hard classes. The concept of Reduction.

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Drill - Practise
  • Problem Solving
  • Proje Design/Management
  • Competences
  • Rational
  • Questoning
  • Use time effectively
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Elementary computing skills
  • Decision making
  • Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%) 0
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