Introduction to Machine Learning: Glossary

The 3 main tasks of Machine Learning are regression, classification and generation.

Machine learning has many applications in high energy physics.

If you want to become proficient in machine learning, you need to practice.

In a particular machine learning problem, one needs an adequate dataset, a reasonable model, and a corresponding loss function. The choice of model and loss function needs to depend on the dataset.

Gradient descent is a procedure used to optimize a loss function corresponding to a specific model and dataset.

Beware of overfitting!

Neural networks consist of an input layer, hidden layers and an output layer.

TensorFlow Playground is a cool place to visualize neural networks!

You’ll be back.

They’re the jedi of the sea.

NumPy and pandas are the main libraries for scientific computing.

scikit-learn and PyTorch are two good options for machine learning in Python.

One must know a bit about your data before any machine learning takes place.

It’s a good idea to visualise your data before any machine learning takes place.

One must properly format data before any machine learning takes place.

Data can be formatted using scikit-learn functionality; using it effectively may take time to master.

Breaks are helpful in the service of learning

Random forests and neural networks are two viable machine learning models.

It’s a good idea to check your models for overfitting.

Many metrics exist to assess classifier performance.

Making plots is useful to assess classifier performance.

It’s a good idea to check whether our machine learning models behave well with real experimental data.

That’s it!

TensorFlow is another good option for machine learning in Python.

The same algorithm can do fairly well across different datasets.

Different optimisation is needed for different datasets.