Research Methods and Advanced Research Topics in AI
6 Credits (ECTS)
Research Methods and Advanced Research Topics in AI
Get 70% back via Tax Credit
€683 refund on this module
Module Description
This module is structured into two distinct parts, each aimed at providing learners with essential knowledge and skills in different areas of study.
The first part of the module focuses on equipping learners with a foundational understanding of Research Methodology. Here, participants will explore key concepts such as Research Problem Identification, Formulation of Research Objectives, Determination of Research Type, Formulation of Research Hypothesis, Selection of Research Approach, Determination of Research Strategy, and Various Data Collection Methods.
This part of the module serves as a crucial primer for students, laying the groundwork for conducting rigorous and methodologically sound research projects.
The second part of the module, learners will delve into Advanced Data topics in Artificial Intelligence. This segment is designed to provide participants with in-depth insights into cutting-edge AI concepts and techniques that are increasingly relevant in today’s digital landscape. Topics covered may include but are not limited to Machine Learning algorithms, Deep Learning, Natural Language Processing, Computer Vision, and Reinforcement Learning. Importantly, the content of this part of the module is tailored to assist students in selecting dissertation topics aligned with their academic and professional interests.
By completing both parts of the module, learners will not only acquire a solid foundation in Research Methodology but also gain exposure to cutting-edge advancements in Artificial Intelligence. Armed with this knowledge, participants will be well-prepared to undertake independent research projects and make meaningful contributions to their respective academic and professional domains.
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:
- Exhaustively evaluate Research problem, Research Objectives and Research Type etc.
- Select a research problem from different sources.
- Identify and Understand the need for literature review and how to do a good literature review.
- Judiciously examine the research hypothesis.
- Appreciate the different research approaches and research strategies.
- Rigorously assess different data collection methods.
- Critically analyse various Advanced Research Topics such as Game Theory and Expert Systems etc
Knowledge:
At the end of the module/unit the learner will have been exposed to the following:
- Identify and discuss Research Methodology and Research Problem.
- Explore the methods for literature review.
- Defining/formlulate The Research Hypotheses, Approach and Strategy.
- Identify and discuss Data Collection Methods and Sampling.
Key indicative topic areas cover:
- Research Methodology and Research Problem
- Review of Literature
- The Research Hypotheses, Approach and Strategy
- Data Collection Methods and Sampling
- Fundamentals issues in advanced AI
- Basic and Advanced Search Strategies
- Reasoning under uncertainty
Skills:
At the end of the module/unit the learner will have acquired the following skills:
- Exhaustively evaluate the knowledge, research problem, research objectives, and research type to select a research problem from different sources.
- Identify and Understand the need for a literature review and how to do a good literature review.
- Judiciously examine different data collection methods.
- Rigorously assess basic and advanced search strategies such as hill climbing, min-max and A* etc.
- Critically evaluate and apply reasoning under uncertainty including probability and knowledge representation.
Judgment Skills and Critical Abilities:
The learner will be able to:
- Evaluate research methods and critically appropriate to discipline being studied.
- 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.