MSc in Data Sciences and Business Analytics - Machine Learning Part 2/7 - Chloé-Agathe Azencott

Описание: Msc in Data Sciences and Business Analytics is a brand new program proposed and coordinated by CentraleSupélec and ESSEC Business School . Check all details here: http://bit.ly/1JnOF5H

Here is the Machine Learning course (First Lecture) by Prof. Chloé-Agathe Azencott, Centre for Computational Biology, Mines ParisTech.

September 2015

[Part 2/7]: Classes of machine learning problems

Machine learning lies at the heart of data science. It is essentially
the intersection between statistics and computation, though the
principles of machine learning have been rediscovered from many
different traditions, including artificial intelligence, Bayesian
statistics, and frequentist statistics. In this course, we view machine
learning as the automatic learning of a prediction function given a
training sample of data (labeled or not).

Machine learning methods form the foundation of many successful
companies and technologies in multiple domains. Their applications, to
name a few, include search engines, robotics, bioinformatics analyses of
genetic data, algorithmic trading, social network analysis, targeted
advertising, computer vision, or machine translation.

This course gives an overview of the most important trends in machine
learning, with a particular focus on statistical risk and its
minimization with respect to a prediction function. A substantial lab
section involves group projects on data science competitions and gives
students the ability to apply the course theory to real-world problems.


Machine learning lies at the heart of data science. It is essentially
the intersection between statistics and computation, though the
principles of machine learning have been rediscovered from many
different traditions, including artificial intelligence, Bayesian
statistics, and frequentist statistics. In this course, we view machine
learning as the automatic learning of a prediction function given a
training sample of data (labeled or not).

Machine learning methods form the foundation of many successful
companies and technologies in multiple domains. Their applications, to
name a few, include search engines, robotics, bioinformatics analyses of
genetic data, algorithmic trading, social network analysis, targeted
advertising, computer vision, or machine translation.

This course gives an overview of the most important trends in machine
learning, with a particular focus on statistical risk and its
minimization with respect to a prediction function. A substantial lab
section involves group projects on data science competitions and gives
students the ability to apply the course theory to real-world problems.