Andres Alonso Toledo Carrera

Ask Andres questions about KTH and read more about his decision to study at KTH, thoughts about Sweden, advice to prospective students and his plans for the future.

Tjena! I’m Andrés Toledo and I am currently enrolled in the second year of the Master’s programme in Machine Learning. I obtained my Bachelor’s degree in Mechatronics Engineering from Tecnológico de Monterrey Campus Estado de México back in 2014. I turned 30 years old this year and celebrated my birthday in Stockholm, which was an amazing birthday gift. I love music, sharks and I’m interested in having conversations with different people, learning about new things and engaging in outdoor activities.

Why did you choose this master’s programme at KTH?

I held an engineering position in the automotive industry for five years, analysing and debugging data from powertrain control modules. I had been thinking about doing a full-time master’s programme for some time and, with the incursion of A.I. in both this industry and other industries, I decided that I wanted to be part of these future developments. When the time came to select a university, KTH stood out from the rest: apart from being considered one of the best universities in engineering and technology, KTH also gave me the opportunity to choose different courses depending on my interests, either specialising in a certain track or learning different implementation techniques.

It is important to mention that living abroad in a vibrant city like Stockholm and getting to know Swedish culture were key aspects of my decision.

How do studies at KTH differ from your previous studies?

Although I am used to working on projects and assignments in teams, I have never worked in an environment as diverse as KTH, sharing similar objectives with my classmates but sometimes using different perspectives and methodologies. Furthermore, students here are encouraged to work at their own pace, instead of spending mandatory time in classrooms. Also, last semester we adopted a more digitally-orientated teaching structure, which brings several advantages to scheduling your life activities.

What are the best aspects of your programme?

Apart from all the technical knowledge I have acquired over the past year, KTH and its community have been very supportive when it comes to academic inquiries. Staff, teachers and students all reach out to help others when necessary. The courses are project-orientated for the most part, which helps you to better understand real-world applications.

Have you chosen a specialisation track within the programme? If Yes, which track and why?

The Master’s Programme in Machine Learning has no specialisation track. However, this also gives you the liberty to choose whichever electives are included in the course list (or even those not included in the course list). I have primarily focused on Deep Learning and plan to include a course in Music Informatics for my second year. Regardless of the chosen courses, all of them have provided me with skills that will further develop my academic career.

How is student life in Stockholm?

As Stockholm is a capital city, there is always something for everyone here: it is pretty diverse and you will get to meet people from different backgrounds. I find this really fulfilling. It is really easy to get around Stockholm because of its excellent public transport system and biking infrastructure. This also gives me the opportunity to take part in many activities, both inside and outside school.

What would you like to say to students thinking of choosing KTH for their master’s studies?

I have had an excellent experience at KTH, both academically and socially. At KTH, you can further develop yourself professionally with great professors and facilities. However, there are also many different activities that are promoted by the school that help with integration, ensuring that the time spent at KTH is of the utmost quality. I have no doubt that anyone who enrols at KTH will be positively affected by this experience.

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Master's programme in Machine Learning