Warsaw, Poland

Machine Learning

Master's
Table of contents
abstract-technology-artificial-intelligence-ai-digital-binary-data-big-data-concept

⇑Machine Learning at UW

Language: EnglishStudies in English
Subject area: engineering and engineering trades
Kind of studies: full-time studies
University website: en.uw.edu.pl

⇑Test: Is Machine Learning the right fit for you?

data-scientists-analysing-model-training-curves-on-monitors

10 questions · two minutes

1. Your model performs brilliantly on the training data but fails on new data. What do you do?

2. How do you feel about the mathematics behind machine learning – linear algebra, probability, optimisation?

3. A new research paper claims a better model architecture. What’s your next step?

4. The data used to train a recruitment model turns out to be biased. How do you respond?

5. How do you feel about spending days cleaning, labelling and exploring data before any modelling?

6. The programme is taught in English, and you’ll work on research projects with students from many countries. How do you feel?

7. Training your model takes far too long on the available hardware. What do you do?

8. Which thesis project would you choose?

9. How do you feel about running models in production – monitoring drift, retraining and versioning?

10. What motivates you most to pursue a Master’s degree in Machine Learning?

⇑Definitions and quotes

Machine
A machine uses power to apply forces and control movement to perform an intended action. Machines can be driven by animals and people, by natural forces such as wind and water, and by chemical, thermal, or electrical power, and include a system of mechanisms that shape the actuator input to achieve a specific application of output forces and movement. They can also include computers and sensors that monitor performance and plan movement, often called mechanical systems.
Machine Learning
Machine learning is a field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
Machine
Looking back over the last thousand years, one can divide the development of the machine and the machine civilization into three successive but over-lapping and interpenetrating phases: eotechnic, paleotechnic, neotechnic … Speaking in terms of power and characteristic materials, the eotechnic phase is a water-and-wood complex: the paleotechnic phase is a coal-and-wood complex… The dawn-age of our modern technics stretches roughly from the year 1000 to 1750. It did not, of course, come suddenly to an end in the middle of the eighteenth century. A new movement appeared in industrial society which had been gathering headway almost unnoticed from the fifteenth century on: after 1750 industry passed into a new phase, with a different source of power, different materials, different objectives.
Lewis Mumford, Technics and Civilisation (1934), 109.
Machine
We are becoming the servants in thought, as in action, of the machine we have created to serve us.
John Kenneth Galbraith (2007) The new industrial state. p. 9.
Machine
It’s possible to imagine a machine that could scoop up material – rocks from the Moon or rocks from asteroids – process them inside and produce just about any product: washing machines or teacups or automobiles or starships. Once such a machine exists it could gather sunlight and materials that it’s sitting on, and produce on call whatever product anybody wants to name, as long as somebody knows how to make it and those instructions can be given to the machine.
Theodore Taylor (1978) as quoted in Nigel Calder, Spaceships of the Mind, Viking Press, New York, 1978; quoted in Robert A. Freitas Jr., Ralph C. Merkle, Kinematic Self-Replicating Machines, Landes Bioscience, Georgetown, TX, 2004
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