Tatiana took the course in 2018-2019. She went on to become a machine learning scientist at AstraZeneca in Cambridge.
Before Cambridge I did a joint Bachelor’s degree in Computer Science and Mathematics at the University of St Andrews. I had a solid background in programming and had taken part in a couple of software engineering internships. During these I was exposed to machine learning and I saw how machine intelligence could solve difficult problems. I found it fascinating and wanted to learn more, which motivated me to apply for this course.
What did you get out of the course?
I gained a broad understanding of machine learning, its practical application and recent topics of research. The greatest benefit, however, was that I learned to read and understand related research publications and work on my own research ideas during the final project. Being in a small class of seventeen meant that it was really easy to study together and benefit from each other's knowledge and experience.
What were the highlights?
I really enjoy teamwork, so one of my favourite parts of the course was conducting a project in a small group. I found that working closely with others meant I was a lot more creative and that discussing ideas was an important part of the research process. Another highlight was taking part in a poster session about our summer projects and discussing them with guests from the industry. They seemed really interested and provided useful tips on how to approach some of the research questions. One of the non-academic highlights was going as a class to each other's colleges for formal dinners. It was a great way to spend time together and visit other colleges!
What have you gone on to do after the course and what are your longer-term plans?
I became a machine learning scientist at AstraZeneca, a pharmaceutical company based in Cambridge. The goal of my research here is to use machine learning to make the drug discovery process more efficient. There are a lot of healthcare areas that have not been explored with machine learning and this makes it very exciting. For now, I would like to stay on the applications side but I might consider doing a PhD in the future.
What advice would you give to people applying for the course?
If machine learning interests you and you meet the course entry requirements, I would say definitely apply, even if you are not sure whether you have exactly the right background. For example, in my class there were people from a range of numerically-based academic backgrounds who all did really well. On the other hand, in my opinion, it is very important to be at least a little familiar with machine learning, understand why it is something you want to pursue and show this clearly in your application.
What advice would you give to people taking the course?
Because there are a lot of topics to be covered in one year, it can get quite intense! The course introduces machine learning from scratch but it is very important to be comfortable with the relevant maths concepts. Staying on top of the workload can get difficult sometimes and so it is crucial to manage your time effectively. I also think it is quite important to stay open-minded as you might become interested in areas of the course that you wouldn't expect.
Finally, I think you gain the most if you engage with your classmates and the academic staff, who are approachable and very passionate about their fields.
6th January 2020