Syllabus for Algorithms and Data Structures II

Algoritmer och datastrukturer II

A revised version of the syllabus is available.

Syllabus

  • 5 credits
  • Course code: 1DL231
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Computer Science G2F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2012-03-08
  • Established by: The Faculty Board of Science and Technology
  • Revised: 2012-03-08
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2012
  • Entry requirements: 60 credits of which at least 15 credits in Mathematics, and 30 credits in Computer Science, including Algorithms and Data Structures I.
  • Responsible department: Department of Information Technology

Learning outcomes

In order to pass, the student must be able to

  • use the notation of asymptotic growth of functions and be able to use this notation to describe the complexity of algorithms and computational problems
  • derive equations for the complexity of algorithms and solve such equations
  • work with common algorithmic techniques such as dynamic programming, greedy algorithms, etc.
  • deal with basic problems using graph algorithms, string matching, computational geometry and flow networks.

Content

Asymptotic notation and recurrence equations. Data structures for disjoint sets. Dynamic programming. Greedy algorithms. Graph algorithms such as shortest path and minimum spanning tree. Maximum flow problem in flow networks. Algorithms for string matching, computational geometry, data compression.

Instruction

Lectures, lessons, and exercises.

Assessment

Written exam (3 credits). Assignments (2 credits).

Reading list

Reading list

Applies from: Autumn 2012

Some titles may be available electronically through the University library.