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.
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 writing a dissertation proposal. 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:
- 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.