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Deep minds: How to pursue a degree in Deep Learning

With the emergence of artificial intelligence, the development of self-driving cars, and virtual assistants powered by deep learning– we at Cybernews Academy wanted to investigate the myriad of details that comprises the topic and how to pursue a degree and career in deep learning. Elenora Giunchiglia is our case study for this article as she gave insight into one of her research niché, ‘deep learning with logical requirements.’

But before we begin our story, we must understand the term ‘deep learning.’

Diving deeper

What is deep learning? Deep learning is a subset of artificial intelligence that mimics the human brain. Our brains contain neurons that take in and process large amounts of data; this is essentially how the neural networks that are present within deep learning function. Deep learning models can recognize complex patterns and produce insights and predictions from this data. This facet of AI simulates the human brain and is trained to automate tasks humans could otherwise do. This could be as simple as transcribing a soundbite into text or as complex as detecting cybersecurity threats. We have one excellent example of someone who has studied this topic in depth for many years.

The impact of deep learning

It’s time to meet Eleonora Giunchiglia, a former University of Oxford, UK student and post-doctoral research assistant at Technische Universität Wien, Austria. This researcher explained one of the ways deep learning is so impactful: “The most significant victory of deep learning or the most prominent reason behind its success was that it was easy to learn.” Eleonora suggests that deep learning can be simple once you know what you’re doing. You can write an important neural network or provisional model and get a perfectly functioning model. She expressed that researchers like herself wanted to make deep learning exciting and easy for people so they could contribute to the ever-present issue in the emerging field. Eleonora told Cybernews Academy that her “research area is on how to create deep learning models that, by design, are compliant with a set of requirements.” Like avionics (electronics that apply to aviation) and software engineering, this software must behave by specific criteria to ensure safety, reliability, and compliance with regulations. Although these basic requirements may seem “trivial” to us, they are important as things can go wrong. For example, Tesla cars have had 17 fatalities and 736 crashes since 2019 due to automation issues. She is hoping to avoid these horrific incidents through necessary research. In Eleonora’s words, “The idea of doing something (like contributing to revolutionary new methods in deep learning) that is useful and can impact society drives me to wake up in the morning and think, I’m not doing something useless, I’m doing something that helps.”

Living proof

Eleonora is proof that specialized education in deep learning and computer science is possible as she has received a Ph.D. in computer science and has completed two master’s degrees in computer science and computer science: data science and engineering. She has embarked on multiple research projects surrounding deep learning throughout her studies. Eleonora has written several publications during her time at the University of Oxford, some of which include ‘Exploiting T−norms for Deep Learning in Autonomous Driving,’ ‘Deep Learning with Logical Constraints,’ and ‘ROAD−R: The Autonomous Driving Dataset with Logical Requirements.’ During her master’s at the University of Oxford, she completed her course by writing her dissertation on ‘Deep Learning for Survival Analysis.’ During her second master’s degree, she further developed this research by writing ‘A Deep Recurrent Model for Survival Analysis.’ Not only is Eleonora proof that you can undertake an educational journey in this field, but she is also an example of the work you can do post-graduation.

Pursuing a degree in deep learning

Perhaps you want to follow in Eleonora’s footsteps and revolutionize the world of deep learning. As the post-doctoral researcher herself said, it is a long but rewarding journey. Like Eleonora, you could choose a bachelor’s, master’s, or Ph.D. program that allows you to focus your research on deep learning. Alternatively, you could find a degree focusing predominantly on machine learning and deep learning. As deep learning is a subset of machine learning and a facet of artificial intelligence, you can find a range of degrees surrounding these subjects.

In-person learning

There are courses around the world that focus on machine learning and deep learning. Many of these courses are open to master’s students. However, there are a variety of modules within computing degrees that focus on deep learning, machine learning, and artificial intelligence. Cybernews Academy has outlined a few interesting courses that caught our eye:

University College London- Machine Learning MSc

UCL offers a Machine Learning master’s (MSc) where you can explore the principles of machine learning and deep learning. You will develop an understanding of new techniques in this area alongside an ability to analyze a range of algorithms. You will also acquire new approaches to design, develop, and evaluate appropriate algorithms. Here you can choose a range of elective and optional topics that include ‘applied deep learning.’ This module aims to equip students with an understanding of deep learning methodology and obtain hands-on experience in deep learning applications with modern development tools.

University of Strathclyde- Machine Learning and Deep Learning MSc

The University of Strathclyde offers a Machine Learning and Deep Learning master’s that allows you to develop and design complex machine learning and deep neural network systems for us in the industry. The course focuses on architectures, algorithms, and novel engineering and software technologies. You will take autonomous sensing, reasoning, deep learning modules, and other compulsory subjects. You can also take elective modules and complete an MSc project.

University of Strathclyde- Advanced Computer Science with Artificial Intelligence MSc

Another course offered by the University of Strathclyde is a master’s program in Advanced Computer Science with Artificial Intelligence MSc. This course allows you to understand how artificial intelligence algorithms and technologies are designed, developed, optimized, and applied to meet business objectives. In your first semester, you will undertake a compulsory module on Deep Learning Theory and Practice. In your second year, you will take another compulsory module on Deep Learning in Visual Computing Applications. You can also participate in a research project in your semester.

Carnegie Mellon- Artificial Intelligence or Computer Science BSc

In North America, you have majors and minors studies. So, at Carnegie Mellon University, Computer Science or Artificial Intelligence would be the major you would look to if you are interested in deep learning. You will also have electives and minors, which you can choose independently. A few electives relating to deep learning and machine learning are Neural Computation, Autonomous Agents, Deep Learning Systems: Algorithms and Implementation, Intermediate Deep Learning, and Introduction to Deep Learning.

University of Oxford- Advanced Computer Science MS

Like Eleonora, you could undertake an Advanced Computer Science course at the University of Oxford and specialize in deep learning. As stated prior, Eleonora focused her research on deep learning and wrote her dissertation on ‘Deep Learning for Survival Analysis.’ This course allows you to take modules on advanced topics in machine learning. It aims to provide a foundation for research into the theory and practice of programming. A degree in Advanced Computer Science will teach you a range of different techniques that are robust and forward-looking to provide you with the foundation for a professional career in computing.

Online learning

Online learning is an excellent option for learning a specialization like deep learning and machine learning. There are various online courses from reputable institutions and education providers. Here are a few that sprung to our attention:

Stanford University - Deep Learning

At Stanford Online, you can discover their deep-learning course, where you will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master the theory and see how it applies to the industry. This class is taught in the flipped classroom format, where you will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for advanced discussions and work on projects. This class will culminate in an open-ended final project.

University of Oxford and Said Business School- Artificial Intelligence

This program is a short 6-week study of artificial intelligence. It is self-paced and entirely online, so you can pick it up when you please. This program provides diverse insights and perspectives on the ethical, legal, and social considerations around AI, enabling you to appraise AI technologies critically. It also introduces you to AI mechanics and subdisciplines, including the different types of machine learning, deep learning, and neural networks.

Cybernews Academy Note: Online specializations are usually short courses and don’t account for a full bachelor’s or master’s degree.

Using deep learning

As Deep learning is used in many industries, from businesses to defense systems, it is vital to have professionals that understand this facet of AI. Therefore, pursuing an education in deep learning is invaluable.

Here are just a few examples of where deep learning is used:

  • Automated cars such as Tesla use deep learning to detect objects such as stop signs, objects, and pedestrians.
  • Virtual assistants are featured on devices like Amazon Alexa and mobile phones. They use deep learning to understand natural language and voice commands to help you complete tasks.
  • Chatbots are used across multiple businesses and use deep learning to solve customer issues in moments.
  • Healthcare often employs computer-aided disease detection and computer-aided diagnosis powered by deep learning.
  • Entertainment providers like Netflix, Amazon, and YouTube use deep learning to enhance customer experience by recommending media based on the person’s browsing history, interests, and online behavior.

A career in deep learning

Deep learning has become the driving force of many applications. You could work as a Machine Learning Engineer. BrainStation described this career as “building artificial intelligence systems that deal with huge data sets to generate and develop algorithms capable of learning and eventually making predictions.” You could become a Data Scientist or Deep Learning Engineer who builds in-app assistance for customers or works on user-focused features that help companies create the best user experience possible. Or, like Eleonora, you could work as a Research Associate where you will perform complex research activity under the supervision of a Principle Investigator.

Where to next

If you want to follow in Eleonora’s footsteps and become a post-doctoral researcher, it is possible once you secure the qualifications you need to obtain this role. Eleonora is now working at Technische Universität Wien and is continuing the research she started at the University of Oxford and Università degli Studi di Genova. She is looking into the practical application of machine learning and deep learning and how it can move from a performance-driven model to a model built against a set of requirements. These requirements should include performance but shouldn’t be the only characteristic. She mentioned that interoperability, fairness, and sustainability might also be required when constructing deep learning models. This highly complex topic can only be concluded through rigorous, long-term research. So we at Cybernews Academy are keeping our eyes out for more from Eleonora. If you want to read more about Eleonora’s epic academic odyssey, check out her student story.

We at Cybernews Academy found deep learning to be an extremely compelling topic. The subject is dense and rich, with a range of complex arguments and many practical examples. As we deduce from Eleonora’s intensive five-year journey, deep learning adapts and changes daily. Will you be the student or professional to make revolutionary changes to an already intensely exciting subject?