This lesson is being piloted (Beta version)

Introduction to Machine Learning using Python

Instructor

  • Instructor: Tue Vu, PhD
  • Office: 2119 Barre Hall, Clemson University
  • Office Hours: Wed 8.30-11.30 & Fri 9.00-10.00, Zoom
  • Email: tuev@clemson.edu

Workshop Description

Machine learning is the science of teaching computers to reproduce the assigned procedure without being explicitly programmed. It has been used in many practical applications such as self-driving cars, speech recognition, email spam classification. It has been widely used not only in engineering (hydroinformatics, bioinformatics, genomics, geosciences and remote sensing, mechatronics) but also in economy, health sciences and even in real estates industry. This workshop provides an overall introduction to machine learning specifically with R programming language which utilizes abundance of R statistical packages. Such topics include: (1) Supervised learning (regression analysis, distance-based algorithm, regularization algorithm, tree-based algorithm, Bayes algorithm, support vector machines, artificial neural networks). (2) Unsupervised learning (clustering, dimensionality reduction). The course will also draw from numerous case studies and applications that can be applied in different engineering programs.

Pre-requisite for the course is “Introduction to Python programming”, offered by CITI team.

Course Outline

Topic

Description
Setup Preparing for the course
1. Introduction to Machine Learning What is Machine Learning
2. Introduction to Scikit Learn What is Scikit Learn
3. Data Partition with Scikit-Learn What is Data Partition
4. Evaluation Metrics with Scikit-Learn How do we measure the accuracy of ML model
5. Training Machine Learning model using Regression Method How to train a Machine Learning model using Regression method
6. Training Machine Learning model using Tree-based model How to train a Machine Learning model using Tree-based model
7. Training Machine Learning model using Ensemble approach How to overcome limitation of single ML model?
8. Training Machine Learning model using Model based Prediction What is model based prediction algorithm in ML?
9. Regularization and Variable Selection Why do we need Regularization and Variable Selection in ML model
10. Dimension Reduction What happen when there are lots of covariates?
11. Neural Network How to use Neural Network in Machine Learning model
12. Support Vector Machine How to use Support Vector Machine in Machine Learning model
13. K-Nearest Neighbour How to use K-Nearest Neighbour in Machine Learning model
14. Unsupervised Learning What is Unsupervised Learning in Machine Learning model
15. Mini-Project What do you learn from Machine Learning workshop using scikit-learn?
Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.