I will be teaching a new machine learning class in the Fall 2017.
Mathematical maturity will be assumed. Programming will also be
required but python tutorial(s) will be provided in the first few
weeks of the class. The plan is to survey different machine learning
techniques (supervised, unsupervised, reinforcement learning) as well
as some applications (e.g., recommender systems). We will also focus
on large-scale machine learning and will discuss distributed
computational frameworks (Hadoop and Spark). 

This course will be thaught in English at HEC. 

80-629-17A - Machine Learning for Large-Scale Data Analysis & Decision Making

In this course, we will study machine learning models, a type of
statistical analysis that focuses on prediction, for analyzing very
large datasets ("big data"). In addition to standard models, we will
study models for analyzing user behaviour and for decision making.
Massive datasets are now common and require scalable analysis tools.
Machine learning provides such tools and is widely used for modelling
problems across many fields including artificial intelligence,
bioinformatics, finance, marketing, education, transportation, and

In this context, we study how standard machine learning models for
supervised (classification, regression) and unsupervised learning (for
example, clustering and topic modelling) can be scaled to massive
datasets using modern computation techniques (for example, computer
clusters). In addition, we will discuss recent models for recommender
systems as well as for decision making (including multi-arm bandits
and reinforcement learning).

Through a course project students will have the opportunity to gain
practical experience with the analysis of datasets from their field(s)
of interest. A certain level of familiarity with computer programming
will be expected.