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Challenge

Machine Learning

Machine Learning

An overview of the tools, techniques and purpose of machine learning.
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Our challengeOutcomesPotential progressInformationProviders

Our challenge

The purpose of this challenge is to provide an extensive and comprehensive overview of the tools, techniques, and purpose of machine learning, which has emerged as a powerful and transformative field in the realm of artificial intelligence. Throughout the course, students will actively engage in a diverse range of innovative teaching activities, designed to cultivate a deep understanding of machine learning approaches, particularly focusing on the distinctions between supervised and unsupervised methodologies. By immersing themselves in hands-on projects and practical exercises, students will gain practical experience in applying these techniques to real-world problems and datasets. Moreover, this module aims to foster critical thinking and awareness among students by exploring the effects and implications of machine learning on sustainability and society. By analyzing case studies and examining ethical considerations, students will gain insight into the social, economic, and environmental impacts of machine learning applications. Additionally, the role of explainable AI (XAI) in the adoption and acceptance of machine learning systems will be thoroughly discussed, allowing students to comprehend the importance of transparency and interpretability in decision-making processes. By the end of this module, students will have a well-rounded understanding of the fundamental principles of machine learning, its potential impact on various domains, and the significance of responsible and ethical practices in this rapidly evolving field.

Study format
Hybrid
Application period
22 June 2023 – 12 March 2024
Study period
15 January – 13 April 2024
Credits
7.5 ECTS
Pace
100%
Hosting university
Dublin City University
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Learning outcomes

LO1

Understand the purpose and key applications of Machine Learning.

ESCO SKILLS

Use IT tools

LO2

Distinguish between supervised and unsupervised Machine Learning methods and when and how to apply them.

ESCO SKILLS

Work in teamsUse IT tools

LO3

Understand the concepts and application of machine learning and artificial intelligence in online learning and large data set applications.

ESCO SKILLS

Analyse big dataWork in teamsDigital competencies

LO4

Apply and synthesise the characteristics of different methods of machine learning

ESCO SKILLS

Solving problems

LO5

Evaluate the ethics of Big Data

ESCO SKILLS

Apply strategic thinkingThink creatively

LO6

Investigate the use of training/test data sets

ESCO SKILLS

Analyse big data

LO7

Apply the Cross Industry Standard Process for Data Mining - Crisp-dm framework ML lifecycle Overarching process - iterative process

ESCO SKILLS

Use ICT systems

LO8

Analyse the potential impact of machine learning in the context of sustainability

ESCO SKILLS

Promote responsible consumer behaviour

LO9

Analyse the difference between ML research and real-world analysis needs

ESCO SKILLS

Analyse big data

Potential progress

This graph shows the potential you could reach if you take this learning opportunity and how it fits your goal. You can also make a comparison to your current and potential competencies by pressing the buttons.

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Entrepreneurship, technology and innovation

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Inter-personal skills

Level
Pioneer
Expert
2
Explorer
Teamwork
Non-verbal communication skills
Conflict resolution
Empathy
Verbal Communication skills
Competence

Media and information literacy

Level
Pioneer
Expert
Explorer
3
1
Understanding and analysing numerical and statistical information
Digital competence
Analysing and evaluating media content
Locating and accessing information
Competence

Critical and innovative thinking

Level
Pioneer
Expert
Explorer
1
1
Problem solving
Generate ideas
Innovative thinking
Creative thinking
Ability to learn
Conscientiousness
Competence

Circular economy

Level
Pioneer
Expert
Explorer
1
Financing circular economy
Waste management
Responsible consumption and production
Sharing and reusing
Competence

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Information

The Challenge

A fundamental Machine Learning challenge can develop learners in several ways:

Career Opportunities: Machine Learning is a rapidly growing field, and there is a high demand for professionals with expertise in this area. By acquiring knowledge in fundamental Machine Learning concepts, learners can open up new career opportunities and enhance their employability. Knowing that there is a demand for their skills can be a strong motivating factor for individuals considering learning Machine Learning.

Practical Application: Machine Learning has numerous real-world applications across various industries such as healthcare, finance, marketing, and more. Learning the fundamentals of Machine Learning can help learners understand how it can be applied to solve complex problems and make predictions. This practical aspect can be highly motivating for individuals who are interested in using technology to create impactful solutions.

Innovations and Advancements: Machine Learning is at the forefront of technological advancements and innovations. By studying the fundamentals, learners can gain insights into cutting-edge research and developments in the field. This exposure to exciting advancements and breakthroughs can inspire and motivate learners to explore Machine Learning further.

Problem-Solving and Critical Thinking: Machine Learning involves analyzing data, identifying patterns, and creating models to make predictions or decisions. The process of learning Machine Learning requires learners to develop problem-solving and critical thinking skills. Engaging in challenging problem-solving exercises and seeing the results of their efforts can be highly motivating for learners.

Hosting university

Dublin City University

Dublin City University