Algorithms and data structures

This course covers:

1. Techniques and algorithms: Abstract data types, Recursion, sorting, search, hashing, shortest path, maximum flow, data compression, greedy algorithms, dynamic programming

2. Data structures: array, lists, queues, stack, tree, graph

3. Theory on complexity and asymptotic notation. Time and space complexity

Knowledge

The candidate should:

• know, and be able to explain, various algorithms for sorting, searching and graph theory
• be able to describe and explain various data structures (arrays, linked lists, queues, stacks, trees and graphs)
• understand the recursive approach to problem solving and programming
• be able to compare algorithmic complexity, and select the most sustainable solution
• be able to describe compelxity classes and np-completeness

Skills

The candidate should be able to:

• use several known algorithms to solve practical programming problems in an efficient way
• write code solving advanced and complex problems
• handle advanced data structures, particularly trees and graphs

General competence

The candidate should be able to combine standard algorithms to make bigger program units.

Lectures & exercises