Syllabus:
Statistical machine learning is a growing discipline at the intersection of computer science and applied mathematics (probability / statistics, (...)
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Statistical machine learning is a growing discipline at the intersection of computer science and applied mathematics (probability / statistics, optimization, etc.) and which increasingly plays an important role in technological innovation.
Unlike a course on traditional statistics, statistical machine learning is particularly focused on the analysis of data in high dimension, as well as the efficiency of algorithms to process the large amount of data encountered in multiple application areas such as image or sound analysis, natural language processing, bioinformatics or finance.
The objective of this class is to present the main theories and algorithms in statistical machine learning. The methods covered will rely amongst others on convex analysis arguments. The practical sessions (more than half of which will be realized with computers) will lead to simple implementations of the algorithms seen in class and with applications to various domains such as computer vision or natural language processing.
Prerequisite: probability theory (notion of random variables, convergence of random variables, conditional expectation), coding skills in python.