Algorithmic Toolbox

Techniques before tools

The course organises its material around four families of technique: ordering and lookup, splitting problems into parts, greedy choices and dynamic programming. Each one is presented as a way of breaking a computational problem into something solvable.

Theory with practice

The description pairs theory with coding practice. Its examples include a genomic application of dynamic programming, which shows how the technique appears in real data work. The listed skills also include debugging and software testing.

Who should take it

• Programmers who want a compact survey of core algorithmic techniques.
• Learners curious about where dynamic programming shows up in genomics.

Treat each technique as a pattern to recognise rather than a single problem to solve.