Descriptif

Objectives

Statistics is the essence behind data science. It is clearly essential to have a deep understanding of the theory and the methods. This is a prerequisite before following a machine learning course.

Syllabus

• Elements of decision theory: risk, loss, decision rules
• Optimal decisions, unbiasedness, equivariance, sufficient statistics
• Pointwise estimator: Z-estimator, M-estimator
• Asymptotical results: law of large numbers, central limit theorem, consistency, asymptotic normality
• Maximum likelihood, Fisher information, Kullback Leibler, asymptotic optimality
• Tests: definitions, the Neyman-Pearson lemma, Uniformly Most Powerful test, p-value

Langue du cours : Anglais