Why Study Algorithms - Foundations of Algorithms 2026s1 - Lecture 1 скачать в хорошем качестве

Why Study Algorithms - Foundations of Algorithms 2026s1 - Lecture 1

The University of Melbourne's Introduction to Algorithmic Thinking: https://algorithmsare.fun This lecture introduces algorithms as machine-independent methods for transforming input into output, and explains why algorithmic thinking matters beyond any particular programming language or machine. It introduces Big O and asymptotic analysis, and examines runtime, average versus worst-case behaviour, data structures and the limits of relying on faster hardware. The second half shifts to C in practice, covering compilation, executables, basic input and output, format specifiers, compiler warnings, character encoding, integer representation, overflow, and the limits of numeric representation. 0:00 Welcome and course framing 7:13 What algorithms are and why they matter 12:06 Enigma, codebreaking, and the substring problem 18:10 Running the first implementation 23:01 Walking through the naive algorithm 29:05 Comparing algorithms with Big O 35:02 Analysing the repeat finder 39:09 Runtime estimates and why hardware alone is not enough 44:02 Average case versus worst case 48:02 Space-time tradeoffs and data structures 51:02 Suffix arrays: building the structure 56:14 Analysing the suffix-array approach 1:06:02 Benchmarking the faster version 1:12:12 Parallelism and why some algorithms do not parallelise well 1:17:15 Algorithms in the real world: C and compilation 1:23:00 What the compiler produces and how programs run 1:32:00 Basic input and output with scanf 1:40:22 Fixing format specifiers and compiler warnings 1:47:05 Types, characters, and ASCII 1:50:10 Bits, overflow, and numeric limits 1:58:48 Floating point and the limits of computation