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 health. 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.