Course Description 

In this course, students will learn about the basics of machine learning. Students will learn the fundamentals of machine learning including regression and classification techniques.  Students will with a variety of learning algorithms and gain the intuition for how they work.  The focus of the course will be on practical skills that can help them train and evaluate models.

Student will also learn about hyperparameter optimization, a technique used to optimize machine learning algorithms to boost the accuracy and performance of trained models. Then students will dig into k-fold cross-validation to perform more rigorous testing for machine learning models.

Throughout this course, students will also build an understanding of what is happening in the model training process with an introduction to sci-kit learn, which is an open-source machine learning library for the Python programming language. Additional Python packages useful to the machine learning pipeline will be utilized.

 

Required Textbooks 

• Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan; 

https://alex.smola.org/drafts/thebook.pdf

• The Mechanics of Machine by Terence Parr and Jeremy Howard

https://mlbook.explained.ai/

• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trever Hastie and Robert Tibshirani

https://www.statlearning.com/

 

Learning Objectives

On completion of this course, students will be able to:

• Understand and explain the basics of machine learning.

• Understand the common pitfalls in machine learning and how to avoid them.

• Understand the key ideas from calculus for understanding how mathematical functions behave.

• Understand the key ideas to understand linear systems.

• Apply the concepts to machine learning techniques.

• Make predictions using the linear regression machine learning model.

• Select, clean, and transform features.

• Apply two different ways of fitting a linear regression model.

• Develop and evaluate a simple classification model