Machine Learning Crash Course: Part 2

Rhodes Artificial Intelligence Lab (RAIL)

Over 3 consecutive Sundays, we are facilitating a Machine Learning Crash Course developed by Google for their engineers. The course videos, materials, and exercises are available <A HREF="https://developers.google.com/machine-learning/crash-course/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">here</A>.
The course is aimed at people who:
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are graduate or 4th year students at Oxford
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have a technical background, and who have some basic coding ability, but who are not yet experienced with machine learning in particular.
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intend to work on a RAIL project starting in Hilary term 2019
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This will allow you to skill-up in advance of the coding/ML for social good projects which begin in Hillary term.
The second session will run from 10am-4pm on Sunday 28th October, including a 1 hour break to go and get lunch. Please note the venue change - this session will be at the Blavatnik School of Government, not the Mathematical Institute.
Every session is facilitated by some excellent former Oxford Machine Learning graduate students. As you go through the material, they will be there to help you and answer any questions you have. This makes completing the course with RAIL much easier and more efficient than trying to cover the content completely on your own.
If you wish to attend this session and be a part of RAIL but did not attend the first session on Sunday 21st October, then you are welcome to do so, but we expect you to have made your way through at least some of the course in your own time beforehand.
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<H2>Pre-Requisites</H2>
This Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, we recommend that students meet the following prerequisites:
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Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms. (Familiarity with more advanced math concepts such as logarithms and derivatives is helpful, but not required.)
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Proficiency in programming basics, and some experience coding in Python. Programming exercises in Machine Learning Crash Course are coded in <A HREF="https://www.python.org/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Python</A> using <A HREF="https://www.tensorflow.org/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">TensorFlow</A>. No prior experience with TensorFlow is required, but you should feel comfortable reading and writing Python code that contains basic programming constructs, such as function definitions/invocations, lists and dicts, loops, and conditional expressions.
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Blavatnik School of Government

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