Maths for AI

MQF Level 7

6 Credits (ECTS)

Maths for AI

Start
October 2024
Module Type
Compulsory
Price
€975
ECTS Credits
6 Credits (ECTS)
Get Qualified Logo

Get 70% back via Tax Credit

€683 refund on this module

Module Description

The module is divided into two parts:

The first part of the module will equip learners with a basic understanding of

  • Set Theory
  • Functions and Relations
  • Permutations and Combinations
  • Probability Theory
  • Differentiation, and Integration, etc.

The second part of the module will equip learners with the basics of Linear Algebra and Matrices, and Linear Transformations etc.

It includes basics operations to be performed on matrices such as addition, subtraction, and multiplication, etc.;

Linear Independence and Dependence of Vectors; and Linear Transformations.

Entry Requirements

Candidates who apply for this course must possess one of the following: 

  • a Level 6 degree related to AI/Computer-Science/Mathematics/Electronics; 

OR 

  • a Level 6 degree not related to AI/Computer Science/Mathematics/Electronics and a minimum of two years’ relevant experience; 

 OR 

  • a Level 5 diploma or higher diploma and five years’ of relevant work experience.   

Target Audience

This course is targeted at: 

  • Industry professionals working in different domains, including Technology, Engineering, Science, IT, Finance, Accountancy, Management, Marketing, Insurance, Banking, Gaming, Healthcare, Medicine, Pharmaceutical, Human Resources, Psychology, Blockchain, Legal, Administration, Policy Making, Digital Art, Archaeology, Architecture, Education and other related areas. 
  • Recent graduates with degrees in Computer Science, Technology, Marketing, Finance, Economics, Accountancy, Management, HR, Law, Engineering, Science, Medicine, Psychology, Digital Art, Game Development, Archaeology, Architecture or Business. 
  • Mid-career-break professionals looking for opportunities to return to or change their career.  

The target group may also be extended to positions such as that of wedding manager, transport manager, maintenance manager, operations manager, marketing manager, conference manager and even that of general manager. 

Module / Unit Instructions

The proposed structure comprises a blended approach promoting the building of a community of practice via peer-to-peer learning.

The structure uses primarily two dimensions of teaching-learning modes:

  1. Face-to-face sessions: 18 hours.
  2. Online Learning Activities: 12 hours.

Face to Face sessions

  • Face-to-face sessions include lectures, tutorials, discussions, presentations and workshop activities promoting peer-to-peer learning.

Online Learning Activities

Online learning activities incorporate tutorials and asynchronous discussions. These may consist of active interaction, participation and contributions in fora discussions, sharing resources and self-reflection exercises.

Learners also contribute to the building of the community of practice by providing feedback to their peers as critical friends, enhancing the learner’s critical engagement throughout the study period.

The tutor provides continual support during both teaching -learning modes by providing information, readings and tasks relevant to the module in question.

The tutor provides continuous formative feedback as an on-going guidance during the student’s learning experience in preparation for their summative assessment.

 

How you’ll be assessed

Assessment of each module consists of two assignments, each carrying a weighting as below:

a) One Formative assignment carries 20% of total module mark achieved. b) One Summative assignment carries 80% of total module mark achieved.

For successful completion of a study module the student is required to achieve a minimum of 41% pass mark in both the formative and the summative assignment.

The overall grade achieved for each module is calculated as the sum of:  20% of the mark achieved for formative assignment; and  80% of the mark achieved for the summative assignment.

All assignment tasks of both formative and summative assessment aim to provide the learner an opportunity to produce evidence of his/her competences aligned to the learning outcomes of each individual Module.

Assessment

a) Formative assessment tasks are provided in the form of structured online discussions that support learners in their development throughout all of the modules studied.

Such discussions are facilitated and monitored by lecturer who provides students with constructive feedback to help them improve and prepare for summative assignment and dissertation.

Formative assessment tasks will contribute to the student’s final mark to acknowledge their work and give chance to improve.

This method allows students to also contribute to the building of the community of practice by providing feedback to their peers as critical friends, enhancing the learner’s critical engagement throughout the study period.

b) Summative assessment is done via one assignment at the end of each module. The mode of assessment varies and may include in-class assignments and home-based written assignments.

For this module , the recommended assessment tool is case studies and proposals. Other assessment tools which may be used are online quizzes (MCQs) Word count range: 3000 ± 10% (except quizzes)

For successful completion of a study module, the student is required to achieve a minimum of 41% pass mark in the summative assignment.

Assignment
Discussions

Module Intake Dates

October 2024
Price
€975

Additional Info

Reading for the entire Master of Science (M.Sc.) in Artificial Intelligence as presented in this brochure costs €9,775.*  

Upon successful completion of this course, students will be eligible for a 70% refund of the cost through the ‘Get Qualified’ scheme.** 

Due to the modular structure of the course, you may also opt to take individual modules as stand-alone. The entry requirements still apply.***  

*Prices are applicable to students who reside in Malta at the time of applying. 

**Terms and conditions apply.  

*** For the price of individual modules, please contact the IDEA Academy team.  

Learning Outcomes

Competences:

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

  • Rigorously assess the need for applications of Set Theory, Functions and Relations for Artificial Intelligence.
  • Understand Permutations & Combinations and Probability Theory.
  • Thoroughly evaluate the requirements of Differentiation and Integration.
  • Proficiently use the matrices algebra such as matrix addition, subtraction, and multiplication, etc.
  • Analyse the problems and challenges in Cayleg Hamilton Theorem.

Knowledge:

At the end of the module/unit the learner will have been exposed to the following:

  • Discuss Set Theory, Relations, and Functions · Identify and discuss theory related to Discrete Mathematics.
  • Compute and contrast implications/elements for Matrix Algebra and Linear Algebra · Explain Linear Transformation and Singular and Non- Regular Linear Mapping.

Key indicative topic areas cover:

  • Set Theory, Relations, and Functions
  • Discrete Mathematics
  • Differentiation, Integration, and Introduction to Vectors
  • Matrices & Determinants
  • Matrix Algebra and Linear Algebra
  • Linear Transformation

Skills :

At the end of the module/unit the learner will have acquired the following skills:

  • Appropriately apply matrices determinants such as Eigen Values & Eigen Vectors, Diagonalization of a Matrix, etc.
  • Understand and apply the fundamentals of Matrix Algebra and Linear Algebra such as groups, rings, and Vector Spaces.
  • Apply the fundamentals of Linear Transformation.
  • Apply Cayleg Hamilton Theorem to analyse the problems and challenges critically.

Judgment Skills and Critical Abilities:

The learner will be able to: ·

Master problem- solving skills by analysing case studies and working out the issues presented in real-life situations.

Module-Specific Communication Skills:

The learner will be able to:

  • Communicates ideas, problems, and solutions to both specialist and non-specialist audiences using a range of techniques involving qualitative and quantitative information to sustain arguments.

Module-Specific Learner Skills:

The learner will be able to:

  • Undertake independent and self-directed study through primary and secondary research.

Module-Specific Digital Skills and Competences:

The learner will be able to:

  • Navigate through the online learning platform to find assignments, discussion boards, literature, tutorials etc.
Accredited
International
Get Qualified
Skip to content