Machine-Learning-from-Theory-to-Practice

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Machine-Learning-from-Theory-to-Practice

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This course will introduce the student to classic machine learning algorithms and deep neural network structures. The style will be first to describe the theory and math behind algorithms and then demonstrate how to use Python to create and run the models.

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# Machine Learning from Theory to Practice
[![DOI](https://zenodo.org/badge/551128506.svg)](https://doi.org/10.5281/zenodo.18738040)

### Objectives:
This applied data science module aims to covers the theoretical, computational and statistical
underpinnings of the machine & deep learning techniques. Applications of different algorithms with an
emphasise on economics and finance will be discussed. Statistical techniques and learning methods that can
lend itself to patterns and relationships in data will be introduced in this module. 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. 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. Integration of these algorithms
to business analytics frameworks will be demonstrated using real-world examples. 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 machine and deep
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, 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 Python to create and run the models. This course will introduce the student to
classic machine learning algorithms and deep neural network structures, Autoencoders, Convolution Neural
Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General
Adversarial Networks (GAN) and reinforcement learning. High Performance Computing (HPC) aspects
will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well
as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to
mathematical foundations.

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