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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
TIN093 - Algorithms  
Algoritmer
 
Syllabus adopted 2020-02-11 by Head of Programme (or corresponding)
Owner: MPALG
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Software Engineering
Department: 37 - COMPUTER SCIENCE AND ENGINEERING


Course round 1


Teaching language: English
Application code: 02122
Open for exchange students: Yes
Block schedule: A
Maximum participants: 135
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0114 Examination 7,5c Grading: TH   7,5c   24 Oct 2020 pm L   05 Jan 2021 am J   26 Aug 2021 pm J

In programs

MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 2 (elective)
MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (compulsory)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 2 (elective)
MPEES EMBEDDED ELECTRONIC SYSTEM DESIGN, MSC PROGR, Year 2 (elective)

Examiner:

Peter Damaschke

  Go to Course Homepage


Course round 2


Teaching language: English
Application code: 02128
Open for exchange students: Yes
Block schedule: A+
Maximum participants: 100

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0114 Examination 7,5c Grading: TH   7,5c   17 Mar 2021 am J,  26 Aug 2021 pm J

In programs

MPHPC HIGH-PERFORMANCE COMPUTER SYSTEMS, MSC PROGR, Year 1 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
TKDAT COMPUTER SCIENCE AND ENGINEERING, Year 3 (elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (elective)
MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 1 (elective)
MPSOF SOFTWARE ENGINEERING AND TECHNOLOGY, MSC PROGR, Year 2 (elective)
MPSOF SOFTWARE ENGINEERING AND TECHNOLOGY, MSC PROGR, Year 1 (compulsory elective)

Examiner:

Birgit Grohe


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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Particularly relevant are an introductory course in data structures and knowledge in discrete mathematics.

Aim

The algorithms course revolves around three central questions that appears naturally when one wants to use a computer to compute a problem:
  • Is the problem computationally solvable at all?
  • How can we do it?
  • How fast can the solution be?

The course shall provide basic knowledge and methods to answer these types of question as precisely as possible, and improve the ability to write fast and well-founded programs.

Other important aspects are: to improve the intuitive sense of time complexity of problems and algorithms in general, to show how the choice of data structures can influence speed of a program, and how data structures must be adapted to algorithms.

The most important goal of the course is to give general principles of creating good algorithms for new problems for which you cannot find any published algorithm.

Learning outcomes (after completion of the course the student should be able to)

  1. Knowledge and understanding
    • describe your algorithms and their qualities: explain algorithms in writing, so that others can understand how they work, why they are correct and fast, and where they are useful.
    • recognize that non-trivial computational problems, which need to be solved by algorithms, appear in various real-world computer applications and to formalize them.
    • intractability: recognize intractable problems and other classes of problems like P, NP, NPC.
    • prove the correctness of algorithms.

  2. Skills and abilities
    • design: apply the main design techniques for efficient algorithms (for instance greedy, dynamic programming, divide-and-conquer, backtracking, heuristics) to problems which are similar to the textbook examples but new.
    • perform the whole development cycle of algorithms: problem analysis, choosing, modifying and combining suitable techniques and data structures, analysis of correctness and complexity, filling in implementation details, looking for possible improvements, etc.
    • perform simple reductions between problems, explain NP completeness, recognize various computationally hard problems which tend to appear over and over again in different applications, cope, at least in principle, with computationally hard problems, using heuristics, refinements of exhaustive search, approximative solutions, etc.

  3. Judgement and approach
    • critically assess algorithmic ideas and demonstrate the ability to resist the temptation to create obvious and seemingly plausible algorithms (which often turn out to be incorrect).
    • analyse: explain why the time efficiency of algorithms is crucial, express the time complexity in a rigorous and scientifically sound manner, analyze the time complexity of algorithms (sum up operations in nested loops, solve standard recurrences, etc.) i.e. perform an objective evaluation of the performance and be able to compare it to other algorithms performance.
However, be aware that this is not a course in programming! The main focus is on the design of algorithms from a given problem specification and the analysis of efficiency of these algorithms. This is, so to speak, the analytical work that has to be done before writing any line of code, if one wants to solve a new problem with the help of computers.

Content

The course topics are as follows:
  • Introduction. What is an efficient algorithm?
  • Tools for analysis of algorithms. O-notation. Analyzing loops and recursive calls. Solving recurrences.
  • Data structures and algorithms. Review of basic data structures.
  • Combining data structures. Merge-and-find.
  • Graph algorithms.
  • Greedy algorithms.
  • Divide-and-conquer.
  • Dynamic programming.
  • Short introduction to local search and approximation algorithms.
  • Basic complexity theory. Complexity classes P, NP, and NPC, reductions. Examples of NP-complete problems. Coping with hard problems.

Organisation

The course is given as lectures, combined with tutorial groups for problem solving related to the course and a number of assignments intended to develop the skill of analyzing and designing algorithms.

Literature

Information about literature is given on the course homepage before course start.

Examination including compulsory elements

The course is examined by an individual written hall-exam.


Page manager Published: Thu 04 Feb 2021.