Material for a UC Irvine course offered by the Department of Physics and Astronomy.
Content is maintained on github and distributed under a BSD3 license.
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ACTIVITY: Discuss these questions:
Using machines to learn how to explain data with models.
Using machines to learn how to explain data with models.
The "machines" responsible for most of the progress in ML are:
The "learning" consists of passively identifying statistical correlations, which is very different from how we learn with active experimentation and identifying causal relationships.
Using machines to learn how to explain data with models.
Machine learning uses models to learn from data.
Further reading:
Data is (are?) a finite set of measurements:
Data is (are?) a finite set of measurements:
Questions to ask about your data:
ACTIVITY: Pick one of these ML problems and describe the rows (samples) and columns (features) of the data you might use to solve the problem.
Two important types of models: generative, probabilistic.
All ML algorithms use a model to explain your data.
Models have parameters.
Two important types of models: generative, probabilistic.
Models can explain data and parameters.
Models have parameters and hyper-parameters.
Three broad types of learning:
(Also: reinforcement learning.)
Scientific applications of ML benefit a lot from advances in industry but we work in a different context:
Physics and astronomy students have different preparation:
Physics and astronomy research also has different needs:
One of the first tasks when applying machine learning to a new problem is to establish some baselines for the expected performance:
Let's get a "human performance" baseline for the following supervised-learning problem: How many "sources" are present in an image?
You are now the machines: