Machine Learning (Écully)

How can a computer beat chess players and go players? How can a bank refine its credit scores to impressive levels, with the same techniques used by the NHS to predict hospital occupancy rates, just like Criteo and Google can predict if you are likely to click on an ad? In the last decades, machine learning has complemented traditional statistics as an important tool to build prediction models, now used routinely in all industries.

This course will teach you the fundamentals of machine learning: principles and practice, using the programming language Python. It will cover Learning theory (statistical ground for learning from data), the main classes of learning algorithms (supervised, unsupervised), the Bias-variance trade-off and overfitting, model training, testing, and validation, and provide an introduction to network analysis, and neural networks (deep learning for image recognition).

This course requires MK34, Python Bootcamp.

Course code: MK340

INSTRUCTOR: Jean SAVINIEN

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