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  3. Algorithmics (MTAT.03.238)
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Algorithmics 2026/27 fall

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    • Why this course?
  • Lectures Wed 10-12
    • G1 Tue 12-14
    • G2 Thu 14-16
    • G3 Fri 14-16 online
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Exam

The 2026/27 exam format, dates, allowed materials, and detailed instructions will be announced later.

We are considering two possible formats:

  • an in-classroom individual exam without course materials, or with only explicitly allowed notes;
  • an individual take-home exam, similar in style to larger homework tasks.

In both cases, the exam is individual work. It is not a group assignment. The exact policy on materials, internet use, and AI assistants depends on the final exam format and will be stated before the exam.

The aim is to ask questions where you have to reason, explain, analyse, and apply course ideas, not merely find a ready-made answer online. Some parts may be more direct, while others may require deeper thinking. Partial solutions can receive partial credit.

The baseline passing level is at least 50% of the exam points.

How to prepare

For repeating the material, make sure you understand the basic concepts from every set of lecture materials and topic notes.

Topics to revise include:

  • Complexity: O, o, Theta, Omega, omega relations between functions, and proofs of such relationships.
  • Recurrences and the master theorem.
  • Sorting algorithms: theoretical and practical considerations.
  • Binary search, array-based linear structures, linked lists, and skip lists.
  • Binary search trees, balanced search trees, and B-trees.
  • Hashing, hash functions, universal hashing, and Bloom filters.
  • Binary heaps and binomial heaps.
  • Tree traversal orders and recursive/non-recursive traversal algorithms.
  • Graph traversal orders and algorithms: BFS, DFS, queue-based and priority-queue-based methods.
  • Shortest paths, Dijkstra, and A*.
  • Topological sorting, maximum flow, and transitive closure by matrix multiplication.
  • Succinct data structures.
  • Optimisation and heuristic search.
  • Metaheuristics: simulated annealing, tabu search, genetic and evolutionary algorithms, differential evolution, ACO, and swarm methods.
  • Dynamic programming.
  • Exact and approximate text matching.
  • Full-text indexing.

Archive: previous exam examples

Past exam tasks may be useful for practice, but the current 2026/27 exam instructions will be announced separately.

2025 home exam

The 2025 exam was a take-home exam from Friday, December 26, 2025 to Thursday, January 8, 2026, 24:00. Students could consult materials and AI tools, but the work had to be done personally, not as group work.

  • 2025 exam tasks description
  • Task 3 sentence list

2024 home exam

The 2024 exam was a take-home exam from December 26 to January 6.

  • 2024 home exam tasks description

Earlier examples

  • Exam tasks for 2023 fall
  • 2023 exam questions and data folder
  • 2023 task 1 solution explanation
  • check this link before publishing
  • Exam tasks for 2022 fall
  • Exam tasks for 2021 fall
  • Exam tasks for 2020 fall
  • Institute of Computer Science
  • Faculty of Science and Technology
  • University of Tartu
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