This lesson is being piloted (Beta version)

Instructor

  • Instructor: Tue Vu, PhD
  • Office: 119 Ford Hall, SMU
  • Email: tuev@smu.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 to have basic Python knowledge and an M2 account (SMU HPC)

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. Supervised Learning with continuous output How to train a Machine Learning model with continuos output
6. Training Supervised Machine Learning model with Categorical Output How to train a Machine Learning model with Categorical output?
7. Dimension Reduction What happen when there are lots of covariates?
8. Neural Network How to use Neural Network in Machine Learning model
9. Unsupervised Learning What is Unsupervised Learning in Machine Learning model
10. Kaggle online competition: Supervised Learning How to participate in a Kaggle online compeition
11. Kaggle online competition: Unsupervised Learning How to participate in a Kaggle online compeition
12. AutoML using Pycaret How to apply Automatic Machine Learning?
13. Data visualization with TSNE How to apply TSNE for data visualization?
Finish

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