Machine Learning
6 Credits (ECTS)
Machine Learning
Get 70% back via Tax Credit
€683 refund on this module
Module Description
This module has two parts. The first part of the module will equip learners with the basic understanding of what Machine Learning is, types of learning and hypothesis space, introduction to R packages and installation procedure.
Learners will also learn various linear regression models and linear functions.
Learners will also get an exposure of some Bayes theorem, Bayes Optimal Classifier and Naïve Bayes classifier etc.
The second part of the module will equip learners with the classification techniques such as K-Nearest Neighbours, Support Vector Machines and Linear SVM formulation.
It will include Solving Machine Learning based Problems with R. Learners will also get an exposure of Neural Networks and Unsupervised learning etc.
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:
- Face-to-face sessions: 18 hours.
- 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 using presentations and case studies.
Other assessment tools which may be used are proposals or reports. Word count range: 3000 ± 10%
For successful completion of a study module, the student is required to achieve a minimum of 41% pass mark in the summative assignment
Module Intake Dates
October 2024
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:
- Appraise and apply Linear regression models and Linear functions and the requirements of Bayesian Learning such as Bayes theorem, Bayes Optimal Classifier and Naïve Bayes classifier etc.
- Exhaustively assess and apply Classification Techniques such as K-Nearest Neighbors, Support Vector Machines, Linear SVM formulation, and Nonlinear SVM etc.
- Rigorously examine and apply Neural Networks such as Multilayer Neural Network, and Deep Neural Network etc.
- Critically analyze Unsupervised learning such as Hierarchical Clustering and K-means Clustering etc.
- Understand the fundamentals of Solving Machine Learning based Problems with R.
Knowledge:
At the end of the module/unit the learner will have been exposed to the following:
- Define and describe types of learning and asic definitions of R and Machine Learning.
- Explore Linear Regression functions · Analyse Bayes theorem and classifiers.
- Discuss solving Machine Learning based Problems with R.
Key indicative topic areas cover:
- Introduction to R and Machine Learning
- Linear Regression
- Bayesian Learning
- Classification Techniques
- Neural Networks
- Unsupervised Learning
- Combining Multiple Classifiers
- Solving Machine Learning based Problems with R
Skills:
At the end of the module/unit the learner will have acquired the following skills:
- Judiciously Combine and use multiple classifiers.
- Apply machine learning and basic data types and data structures in R.
- Thoroughly evaluate various linear regression models and requirements of Bayesian learning.
- Rigorously analyse different classification techniques and neural networks.
- Critically examine and use unsupervised learning approaches such as Hierarchical Clustering and K-means Clustering etc.
Judgment Skills and Critical Abilities:
The learner will be able to:
- Collect, analyse and interpret data/information to support arguments, and to develop and apply ideas.
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.