Study-Unit Description

Study-Unit Description


CODE ARI1103

 
TITLE AI Numerical Methods 1

 
UM LEVEL 01 - Year 1 in Modular Undergraduate Course

 
MQF LEVEL 5

 
ECTS CREDITS 5

 
DEPARTMENT Artificial Intelligence

 
DESCRIPTION This study-unit will provide students with the necessary analytic foundations for the study of Artificial Intelligence. It will familiarise students with different quantities (scalars, vectors, matrices and tensors) and will focus on the operations used on these quantities for different applications within Artificial Intelligence. This study-unit shall also cover differentiation and integration techniques used in Artificial Intelligence.

Study-Unit Aims:

The aims of the study-unit are to:

- Introduce students to scalars, vectors, matrices and tensors;
- Explain the role of the identity matrix;
- Explain how inverse matrices can be calculated and how they are used for different AI applications;
- Introduce the concept of linear dependence;
- Cover differentiation and integration techniques to solve AI problems

Learning Outcomes:

1. Knowledge & Understanding:

By the end of the study-unit the student will be able to:

- work with scalars, vectors, matrices and tensors;
- compute inverse matrices;
- work out linear dependence and span;
- use differentiation and integration techniques to solve problems.

2. Skills:

By the end of the study-unit the student will be able to:

- analyse diverse problems using the right mathematical tools;
- apply the acquired knowledge in linear algebra and calculus to compute solutions to problems.

Main Text/s and any supplementary readings:

Main Texts:

- Deisenroth, M., Faisal, A., & Ong, C. (2020). Mathematics for Machine Learning. Cambridge: Cambridge University Press. doi:10.1017/9781108679930
- Strang, G. (2019) Linear Algebra and Learning from Data. Wellesley-Cambridge Press.

Supplementary Readings:

- Strang, G. (2016) Introduction to Linear Algebra. Wellesley-Cambridge Press.

 
STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Assessment Due Sept. Asst Session Weighting
Quiz SEM1 Yes 30%
Quiz SEM1 Yes 35%
Quiz SEM1 Yes 35%

 
LECTURER/S Matthew Montebello

 

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years.

https://www.um.edu.mt/course/studyunit