اولین دوره آموزشی یادگیری ماشینی (machine learning)

:Objectives

Finding patterns and relationships in large volumes of data are very useful in market research, business forecasting, decision support, and customer recommendation engines among other applications. Artificial intelligence methods that can lend itself to patterns and relationships in data will be introduced in this module. Applications of different deep machine learning algorithms will be discussed. Integration of these algorithms to business analytics frameworks will be demonstrated using real-world examples. This applied data science module aims to covers the theoretical, computational and statistical underpinnings of the machine learning techniques. The size, complexity, and diversity of data increase every day. This means we need new solutions for analyzing data. Big data and statistical learning methods provide a vehicle for modeling and analyzing complex phenomena and for incorporating rich sources of confounding information into economic models. Course demonstrations will be in Python, and for showcases and exercises, we make use of python scientific libraries. We also expose students to Google Colab so they can develop their coding skills by completing practical exercises on Colab. The data sets we will use for this course are from World Bank Group, Kaggle, Federal Reserve Economic Data, Google Finance, and several other resources. For the sake of learning, we will apply the algorithms and topics step by step to the problem, both in standard python libraries and from scratch

:Course Outline

The goal of this module is to give an applied, hands-on introduction to big data machine learning methods. At the end of the course, students will be able to read and understand theoretical papers on the subject, to implement the techniques themselves in Python by using NVIDIA RAPIDS libraries, and to apply the techniques to data used in economics and business. The style will be first to describe the theory and math behind algorithms and then demonstrate how to use RAPIDS library to create and run the models

:Prerequisites

An undergraduate-level understanding of linear algebra and probability analysis