“Machine Learning is the study of computer algorithms that improve through experience”
Machine Learning, Tom Mitchell, McGraw Hill, 1997.
Machine learning is the process of developing computer algorithms that learn from past data. This involves creating a program that uses statistics, calculus and linear algebra to optimize a model to make more informed decisions
Machine Learning Methods
Different types of problems require different types of solutions. While all machine learning architectures automatically learn from past data, some will be better with different data than others. SMLB aims to explore many different machine learning methods such as regressions, decision trees and deep learning. Participating in SMLB challenges will involve determining the best of the established machine learning methods in order to assist businesses.
A linear regression, one of the simplest forms of machine learning
Building a Machine Learning Program
However, it is not always necessary to be well versed in the mathematics in order to implement machine learning in your business. Previously coded python libraries such as scikit-learn and keras allow you to jump into designing machine learning architectures without having to start from ground zero. Learning the basics of these libraries will allow those new to coding to quickly learn how to implement machine learning in business problems that affect our world.
Applications of Machine Learning
Artificial Intelligence is becoming more and more prevalent in our changing business climate. Many business rely on machine learning for product recommendations, financial analysis and ensuring secure transactions. SMLB is the only student group on the UAlberta campus dedicated to building these programs for businesses and determining their best implementations.