Anybody who’s ever given ChatGPT or a similar AI-powered software a whirl has seen machine learning in action. Today, we’re on the cusp of a computational revolution as computer systems are being taught to do more than simply follow processes. They can learn just like humans though they can only do so using algorithms and models designed to show them what to learn and how to draw conclusions.


Those who can master machines, or more accurately, the concepts of building digital brains for machines, stand to enjoy long and lucrative careers. Glassdoor tells us that the average machine learning engineer picks up €70,318 in Germany alone, with senior-level engineers picking up close to €90,000. But to get to the point where you can work in this field, you need a Master’s in machine learning to demonstrate that you know what you’re doing. This article looks at three of the best programs for European and international students.


Factors to Consider When Choosing a Masters in Machine Learning Program


Before we dig into the courses, it’s important to highlight what we’re looking for. After all, a certificate needs to be worth more than the paper on which it’s printed, serving as tangible proof that you have the machine learning chops prospective employers desire.

  • University Reputation – A certificate from a university with a bad reputation is like word-of-mouth from a shyster – nobody trusts it. Any institution you choose needs to have a stellar reputation as a provider of high-quality programs.
  • Course Curriculum – The general concept of machine learning branches off into many different paths and specializations, each of which takes you in different career directions. By examining the course curriculum, you confirm that your program leads you down the right path rather than being something that’ll force you to course-correct in the future.
  • Faculty Expertise – The people who teach you need to have roots in the machine learning sector. Those roots can come from their experience in industry, academic success, or research, but they need to be there if your teachers are to provide the fuel to grow your academic seed.
  • Industry Connections – Machine learning already permeates through any industry that relies on data (i.e., almost all of them), so you want a university that offers links to employers. Look for internship programs, lecturers with a history of real-world experience, and careers departments designed to help you get ahead.
  • Tuition Fees – There’s no getting around the fact that a Master’s degree in any subject sets you back a few thousand euros. How many thousands depends on the nature of your course and the institution, so look for something that’s affordable and (where applicable) can provide financial aid.


Top Masters in Machine Learning Programs


With what to look for established, it’s time to look at a trio of Master’s in machine learning courses that fit the bill when examined under the lens of the above five factors.


Master of Science in Machine Learning and Data Science (Imperial College London)


Imperial College London has always held a high reputation in the UK (it was a fixture on the old show “University Challenge”) and its Master’s degree courses allow you to piggyback off that reputation. This Master’s is a 24-month program that’s offered 100% online, making it as accessible to international students as it is to English ones.


The program starts you off with theory and ethics, helping you understand the programming techniques and math that go into designing machine learning models. By the second year, you’ll start getting your feet wet with practical projects, develop mastery of unsupervised learning, and take on research projects to show you can apply what you’ve learned. The faculty has wide-ranging experience, led by Professor Michael Bronstein, the university’s Chair of Machine Learning and Pattern Recognition. His expertise has been called upon by the likes of the University of Oxford and Project CETI, meaning you’re in good hands from the course creation and guidance perspectives.


The downside is that this is an expensive course, costing international students £16,200 per year for a total of £32,400 (approx. €37,310 as of time of writing). That’s money well spent, considering you get a degree from a university that ranks sixth in the QS World University Rankings and has an alumni network that stretches to over 200,000 former students and faculty members. Financial assistance for those high tuition fees is available for Imperial’s Student Support Fund and Global Relief Fund, though both are only available to students who face unexpected financial hardship.


Master in Management of AI and Machine Learning (UBI Business School)


From a course focused primarily on theory, we move to one that takes a much more business-centric focus. UBI Business School has five-star ratings across the board from QS University World Rankings and delivers courses that help students harness their knowledge to meet the demands of modern industry.


Creating digital leads is the stated goal of the program, which it highlights through a curriculum developed by some of the world’s leading tech companies. The idea is simple – ask companies what they want and let them design a course that teaches it. First-stage students start with modules focusing on the psychology and ethics behind modern technology. By the second stage, those who choose the AI and machine learning specialization move into the fundamentals of AI, neural networks, and applying Python to large datasets. Finally, this MSc machine learning concludes with a management project, where you’ll complete a thesis and work directly either with an existing business or in the university’s Venture Creation Lab.


Tuition may be a sticking point because you need to pay €11,900 for the course, though you can get a discount if you pay upfront. UBI also offers scholarships based on merit and for special groups (i.e., people with special political associations). International students can also benefit from global inclusion and refugee scholarships designed to make education more accessible. The teaching staff, led by Dean and Professor Gaston Fornes, includes people who have over 15 years of professional experience, five of which are spent in senior leadership roles.


Master in Applied Data Science & AI (OPIT)


Don’t let the lack of the term “machine learning” in the degree’s name fool you – OPIT’s course leans heavily into machine learning. In the first term alone, you’ll learn about feature engineering, different machine learning models, and how to visualize data through Python and relevant coding libraries. But you’ll learn all of that in the context of how machine learning applies in data science, making the program ideal for practical people with one eye turned toward a data science career.


That focus on practicality continues in the second team, where you can study the applications of machine learning more directly. The third (and final) term is your thesis, which is your choice between a research project or an internship with a real-world company. Speaking of associations with companies. OPIT’s team of teachers boasts experience working with some major players, with former Google and Microsoft employees among their numbers. Again, that feeds into the applied approach brought to this Master’s in machine learning as you’ll learn from people who’ve actually applied what they’re teaching you.


Tuition fees are also reasonable for this 18-month course. Most can expect to pay €6,500, though early bird discounts are on offer to bring the price closer to the €5,000 range if you apply several months before the October intake. You can also pay in installments.



Other Notable Masters in Machine Learning Programs


The three courses highlighted above all offer something different, with one being more theory focused, another taking on the business angle, and the third falling somewhere in between. But beyond those three, here are a few more good MSc machine learning universities to consider.


Carnegie Mellon University


As one of the world’s top-ranked AI institutions, Carnegie Mellon is ideal for those who want to study in the United States. Learning from top researchers gives you a solid pedigree that makes you more desirable to employers after your studies.


University of Oxford


The University of Oxford’s low 18% acceptance rate belies its reputation as the UK’s foremost academic institution. Simply having the word “Oxford” on your CV opens doors that other degree programs can’t.


KU Leaven


Don’t let KU Leaven’s reputation as one of the oldest Catholic universities in the world trick you into thinking it’s not the best place for the sciences. It’s a world leader in research, especially in AI and biomedical science fields.


Guide the New Wave of Machines With an MSc Machine Learning Degree


By choosing to pursue a Master’s in machine learning, you’ve put yourself on track for a career that will be lucrative and has the potential for enormous growth as more companies adopt AI. You’re also getting yourself in on (or near) the ground floor of a metaphorical building that’s going to be so high that we may not ever see the top.


The three courses here (plus the universities touched upon at the tail end of the article) offer differing paths into machine learning. But all three give you the same result – an MSc machine learning qualification you can use to build a superb career.

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Expert Pierluigi Casale analyzes the adoption of AI by companies, the ethical and regulatory challenges and the differentiated approach between large companies and SMEs

By Gianni Rusconi

Easier said than done: to paraphrase the well-known proverb, and to place it in the increasingly large collection of critical issues and opportunities related to artificial intelligence, the task that CEOs and management have to adequately integrate this technology into the company is indeed difficult. Pierluigi Casale, professor at OPIT (Open Institute of Technology, an academic institution founded two years ago and specialized in the field of Computer Science) and technical consultant to the European Parliament for the implementation and regulation of AI, is among those who contributed to the definition of the AI ​​Act, providing advice on aspects of safety and civil liability. His task, in short, is to ensure that the adoption of artificial intelligence (primarily within the parliamentary committees operating in Brussels) is not only efficient, but also ethical and compliant with regulations. And, obviously, his is not an easy task.

The experience gained over the last 15 years in the field of machine learning and the role played in organizations such as Europol and in leading technology companies are the requirements that Casale brings to the table to balance the needs of EU bodies with the pressure exerted by American Big Tech and to preserve an independent approach to the regulation of artificial intelligence. A technology, it is worth remembering, that implies broad and diversified knowledge, ranging from the regulatory/application spectrum to geopolitical issues, from computational limitations (common to European companies and public institutions) to the challenges related to training large-format language models.

CEOs and AI

When we specifically asked how CEOs and C-suites are “digesting” AI in terms of ethics, safety and responsibility, Casale did not shy away, framing the topic based on his own professional career. “I have noticed two trends in particular: the first concerns companies that started using artificial intelligence before the AI ​​Act and that today have the need, as well as the obligation, to adapt to the new ethical framework to be compliant and avoid sanctions; the second concerns companies, like the Italian ones, that are only now approaching this topic, often in terms of experimental and incomplete projects (the expression used literally is “proof of concept”, ed.) and without these having produced value. In this case, the ethical and regulatory component is integrated into the adoption process.”

In general, according to Casale, there is still a lot to do even from a purely regulatory perspective, due to the fact that there is not a total coherence of vision among the different countries and there is not the same speed in implementing the indications. Spain, in this regard, is setting an example, having established (with a royal decree of 8 November 2023) a dedicated “sandbox”, i.e. a regulatory experimentation space for artificial intelligence through the creation of a controlled test environment in the development and pre-marketing phase of some artificial intelligence systems, in order to verify compliance with the requirements and obligations set out in the AI ​​Act and to guide companies towards a path of regulated adoption of the technology.

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The Lucky Future: How AI Aims to Change Everything
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 10, 2025 7 min read

There is no question that the spread of artificial intelligence (AI) is having a profound impact on nearly every aspect of our lives.

But is an AI-powered future one to be feared, or does AI offer the promise of a “lucky future.”

That “lucky future” prediction comes from Zorina Alliata, principal AI Strategist at Amazon and AI faculty member at Georgetown University and the Open Institute of Technology (OPIT), in her recent webinar “The Lucky Future: How AI Aims to Change Everything” (February 18, 2025).

However, according to Alliata, such a future depends on how the technology develops and whether strategies can be implemented to mitigate the risks.

How AI Aims to Change Everything

For many people, AI is already changing the way they work. However, more broadly, AI has profoundly impacted how we consume information.

From the curation of a social media feed and the summary answer to a search query from Gemini at the top of your Google results page to the AI-powered chatbot that resolves your customer service issues, AI has quickly and quietly infiltrated nearly every aspect of our lives in the past few years.

While there have been significant concerns recently about the possibly negative impact of AI, Alliata’s “lucky future” prediction takes these fears into account. As she detailed in her webinar, a future with AI will have to take into consideration:

  • Where we are currently with AI and future trajectories
  • The impact AI is having on the job landscape
  • Sustainability concerns and ethical dilemmas
  • The fundamental risks associated with current AI technology

According to Alliata, by addressing these risks, we can craft a future in which AI helps individuals better align their needs with potential opportunities and limitations of the new technology.

Industry Applications of AI

While AI has been in development for decades, Alliata describes a period known as the “AI winter” during which educators like herself studied AI technology, but hadn’t arrived at a point of practical applications. Contributing to this period of uncertainty were concerns over how to make AI profitable as well.

That all changed about 10-15 years ago when machine learning (ML) improved significantly. This development led to a surge in the creation of business applications for AI. Beginning with automation and robotics for repetitive tasks, the technology progressed to data analysis – taking a deep dive into data and finding not only new information but new opportunities as well.

This further developed into generative AI capable of completing creative tasks. Generative AI now produces around one billion words per day, compared to the one trillion produced by humans.

We are now at the stage where AI can complete complex tasks involving multiple steps. In her webinar, Alliata gave the example of a team creating storyboards and user pathways for a new app they wanted to develop. Using photos and rough images, they were able to use AI to generate the code for the app, saving hundreds of hours of manpower.

The next step in AI evolution is Artificial General Intelligence (AGI), an extremely autonomous level of AI that can replicate or in some cases exceed human intelligence. While the benefits of such technology may readily be obvious to some, the industry itself is divided as to not only whether this form of AI is close at hand or simply unachievable with current tools and technology, but also whether it should be developed at all.

This unpredictability, according to Alliata, represents both the excitement and the concerns about AI.

The AI Revolution and the Job Market

According to Alliata, the job market is the next area where the AI revolution can profoundly impact our lives.

To date, the AI revolution has not resulted in widespread layoffs as initially feared. Instead of making employees redundant, many jobs have evolved to allow them to work alongside AI. In fact, AI has also created new jobs such as AI prompt writer.

However, the prediction is that as AI becomes more sophisticated, it will need less human support, resulting in a greater job churn. Alliata shared statistics from various studies predicting as many as 27% of all jobs being at high risk of becoming redundant from AI and 40% of working hours being impacted by language learning models (LLMs) like Chat GPT.

Furthermore, AI may impact some roles and industries more than others. For example, one study suggests that in high-income countries, 8.5% of jobs held by women were likely to be impacted by potential automation, compared to just 3.9% of jobs held by men.

Is AI Sustainable?

While Alliata shared the many ways in which AI can potentially save businesses time and money, she also highlighted that it is an expensive technology in terms of sustainability.

Conducting AI training and processing puts a heavy strain on central processing units (CPUs), requiring a great deal of energy. According to estimates, Chat GPT 3 alone uses as much electricity per day as 121 U.S. households in an entire year. Gartner predicts that by 2030, AI could consume 3.5% of the world’s electricity.

To reduce the energy requirements, Alliata highlighted potential paths forward in terms of hardware optimization, such as more energy-efficient chips, greater use of renewable energy sources, and algorithm optimization. For example, models that can be applied to a variety of uses based on prompt engineering and parameter-efficient tuning are more energy-efficient than training models from scratch.

Risks of Using Generative AI

While Alliata is clearly an advocate for the benefits of AI, she also highlighted the risks associated with using generative AI, particularly LLMs.

  • Uncertainty – While we rely on AI for answers, we aren’t always sure that the answers provided are accurate.
  • Hallucinations – Technology designed to answer questions can make up facts when it does not know the answer.
  • Copyright – The training of LLMs often uses copyrighted data for training without permission from the creator.
  • Bias – Biased data often trains LLMs, and that bias becomes part of the LLM’s programming and production.
  • Vulnerability – Users can bypass the original functionality of an LLM and use it for a different purpose.
  • Ethical Risks – AI applications pose significant ethical risks, including the creation of deepfakes, the erosion of human creativity, and the aforementioned risks of unemployment.

Mitigating these risks relies on pillars of responsibility for using AI, including value alignment of the application, accountability, transparency, and explainability.

The last one, according to Alliata, is vital on a human level. Imagine you work for a bank using AI to assess loan applications. If a loan is denied, the explanation you give to the customer can’t simply be “Because the AI said so.” There needs to be firm and explainable data behind the reasoning.

OPIT’s Masters in Responsible Artificial Intelligence explores the risks and responsibilities inherent in AI, as well as others.

A Lucky Future

Despite the potential risks, Alliata concludes that AI presents even more opportunities and solutions in the future.

Information overload and decision fatigue are major challenges today. Imagine you want to buy a new car. You have a dozen features you desire, alongside hundreds of options, as well as thousands of websites containing the relevant information. AI can help you cut through the noise and narrow the information down to what you need based on your specific requirements.

Alliata also shared how AI is changing healthcare, allowing patients to understand their health data, make informed choices, and find healthcare professionals who meet their needs.

It is this functionality that can lead to the “lucky future.” Personalized guidance based on an analysis of vast amounts of data means that each person is more likely to make the right decision with the right information at the right time.

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