Intersect Online Training: Introduction to Machine Learning using R: SVM & Unsupervised Learning

Please register your interest by joining the waitlist using your University email*. Tickets will be allocated to registrants two weeks prior to the course.

* People who register without a University email will not be considered when allocating the tickets. Please make sure that you use a valid email address.

About this course:

Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. We provide a comprehensive introduction to Support Vector Machine and Unsupervised models in Machine Learning and use R to apply the knowledge on real-world datasets. We hope after this hands-on workshop, you will have a better understanding of these Machine Learning models and techniques and appreciate its capability, as well as make better informed decisions on how to leverage Machine Learning in your research.

Learning Outcomes:

• Comprehensive introduction to Machine Learning models and techniques such as Logistic Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.
• Know the differences between various core Machine Learning models.
• Understand the Machine Learning modelling workflows.
• Use R and its relevant packages to process real datasets, train and apply Machine Learning models.

Prerequisites:

• Either Learn to Program: R and Data Manipulation in R or Learn to Program: R and Data Manipulation and Visualisation in R needed to attend this course. If you already have experience with programming, please check the topics covered in the Learn to Program: R, Data Manipulation in R and Data Manipulation and Visualisation in R and Introduction to ML using R: Introduction & Linear Regression courses to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training.
• Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
• For more information about prerequisites and programming pathways for this course, please visit the course page on our website.

Why do this course?

• Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
• It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
• We do have applications on real datasets!
• Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
• Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.

For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops:

• Introduction to Machine Learning using R: Introduction & Linear Regression
• Introduction to Machine Learning using R: Classification
• Introduction to Machine Learning using R: SVM & Unsupervised Learning