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.
Career Paths
The programme prepares for positions such as and not limited to:
- Compliance Supervisor/Manager/Officers
- Anti-Money Laundering Managers
- Money Laundering Reporting Officer and their deputies
- Law enforcement agencies and supervisory and regulatory authorities
- Managers with responsibilities for internal AML controls
- Risk Managers
- Director of Compliance
How you’ll be assessed
The course comprises:
- Evening classes for part-time courses.
- Classes held throughout the day for full-time courses.
- Guided learning, presentations, comprising synchronous online discussions, tutorials and/or videos.
- Self-study hours comprising research, reading and assignment work.
Assessment
Assessment is carried out via two mandatory components:
- Modular Assessment
- Summative Assessment
The programme includes different forms of assessment which allow for and promote students’ critical engagement. The formative and summative assessment tasks may include an in-class assignment and/or a home-based written assignment using diverse assessment tools which may take the form of online and in-class discussions, examinations, case studies, reports, proposals, essays, and presentations, etc., as applicable to the diverse modules.
Additional Info
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.
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.