Introduction to Algorithmics
This course introduces students to the notion of efficiency and computational complexity. The basic data structures encountered in first year, such as lists, trees and graphs, are reviewed in light of their efficiency and common usage scenario. Asymptotic measures of complexity are covered, and recurrence relations are introduced as an analytical tool. Problem-solving techniques such as the greedy strategy, divide-and-conquer, dynamic programming, and graph searching are covered. These techniques are illustrated upon optimization problems chosen for their practical relevance.
- Semester 2 - 2015
1. apply basic techniques to analyse the performance of algorithms;
2. explain the most important algorithms used in various common computer science applications;
3. apply efficient algorithm design techniques and understand the limitations of algorithms.
- Preliminaries (review of basic mathematical notions, data structures, induction, basic combinatorics).
- Elementary algorithmics (worst-case vs. average case, basic examples, elementary operations).
- Asymptotic Notation (big O, Omega and Theta).
- Analysis of Algorithms (loops, recurrence relations).
- Data structures (graphs, trees, heaps, disjoint sets).
- Searching and Sorting.
- Greedy algorithms.
- Dynamic programming.
- Text-search Algorithm.
Introduction to the topics of computational complexity, heuristics, metaheuristics and approximation algorithms.
SENG6120 Knowledge of discrete mathematics
Written Assignment: Essays / Written Assignments
In Term Test: Examination: Class
Formal Examination: Examination: Formal *
Project: Assessment tailored towards MIT students needs
* This assessment has a compulsory requirement.
In order to pass this course, each student must complete ALL of the following compulsory requirements:
Course Assessment Requirements:
- Formal Examination: Minimum Grade / Mark Requirement - Students must obtain a specified minimum grade / mark in this assessment item to pass the course. - Students must obtain 40% in the final exam to pass the course.
Face to Face On Campus 3 hour(s) per Week for Full Term
Face to Face On Campus 2 hour(s) per Week for Full Term