ITO4001 · Foundations of computing

Overview

Mathematics and Computer Science cannot be untangled. Most of the problems found in computer science are often formalized and solved with mathematical foundations. Many important problems addressed by computer scientists need the skills in logical thinking, algebraic operations, probability theory and statistical tests and optimization techniques. This unit is designed to explore various mathematical methods required to work in the area of computer science. You will learn the fundamental concepts in tree and graph data structures, set theory and logic which include predicate and propositional logic, probability and statistics as well as linear algebra, data encoding and calculus. While learning the fundamentals, the unit offers a variety of problem solving activities concentrating on areas where mathematical foundations pave a path to computing problems. It offers an opportunity to understand how to solve computer science problems using mathematical foundations that are relevant to the program the students follow.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. apply discrete mathematical structures, especially graphs and trees to solve problems in computer science.
2. apply propositional logic and predicate logic to problem-solving and set theory to represent collections of data to solve real-life problems.
3. demonstrate an understanding of the fundamentals of continuous probability and statistics, including common Probability Distributions, Bayes’ Theorem and parameter estimation.
4. describe the fundamental concepts relating to linear algebra such as linear functions, vectors, and matrices.
5. demonstrate skills and understanding on calculus, partial derivatives, differentiation, integration, gradients and gradient algorithms.
6. demonstrate understanding on the fundamental concepts in information theory such as entropy, encoding, decoding and data compression.

Workload

No workload detail published.

Student experience, not official rules. Nothing here changes what the Handbook says.