Welcome to MATH80629A Graduate level course on introduction to machine learning at HEC Montreal (English edition). This is the English edition of the course, for the French edition, please check here. 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”). 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 study large-scale machine learning and will discuss distributed computational frameworks (Hadoop and Spark).
Due to the online nature of the semester, this course will be given as a flipped classroom. It is an instructional strategy where students learn the material before they come to class. The material will be a mix of readings and video capsules. Class time is reserved for more active activities such as problem solving, demonstrations, and questions-answering. In addition, class time will contain a short summary of the week’s material.
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Mathematical maturity and basic knowledge of statistics, and probability will be assumed. For the programming assignments and the project, Python programming will be assumed. If you do not know Python here are few ways to learn the basics below.
Further a machine-learning tutorial using python will be provided on week #5.
Your final score for the course will be computed using the following weights:
I thank prof. Laurent Charlin for sharing his slides and video capsules with me. The majority of the materials of this coursse are based on the previous editions that have been thaugh by him.