Machine Learning with R, Part 2

Introduction

This is a public course designed for students in different levels in Umeå University, and it is given at the department of statistics, Umeå University. Students need to have elementary programming knowledge and some understanding of basic statistics, at most understanding regression analysis. In the first part of this course (Part I), we focused on familiarizing students with the basic concepts of machine learning through basic linear models. Now, in the second part of the course, we will focus on nonlinear models. There are many nonlinear models in machine learning, such as kernel methods, ensemble methods, etc. However, we will concentrate on understanding complex artificial neural network models and introduce what is known as deep learning.

In this new era, learning various skills in data science is as straightforward as learning to get a driver’s license, but not everyone can become an engineer or technician for the Ferrari racing team. My visions: First, every student can get their license and add new tools to their data analysis toolbox. Second, I hope this course can provide some helps to those who want to understand the engine behind the tools. If that sounds like you, you’ll need to invest some time and a bit of patience :)

Course Design

Next, I will use a mini slides to illustrate the design of my course.

Slides for course design

TextBook

We use ‘An Introduction to statistical learning’ as our textbook. The website of the book: https://www.statlearning.com. On this website, you can not only get an electronic copy of this book, but also find a lot of useful information.

Teaching Methods

As an online course, we naturally choose the flipped classroom teaching method. That is, students first read and study the materials and textbooks we provided, and then conduct laboratory lessons after discussions in a one hour recap session.

Examination Methods

We use a combination of project study and oral interviews to assess students’ mastery of course knowledge.

Tips for your readings ( CA )

1. New~ In this course, for better readability, I will introduce embedded slides like the one above about course design. Therefore, when you see windows with light green borders, please don’t mistake them for simple images and overlook the important information.

2. For each lecture, I offer multiple ways to read. You can choose the integrated notes, allowing you to scroll up and down with ease, and use the side menu to jump between sections. However, if you find lengthy notes overwhelming, you might prefer the paginated version. It’s like how I divide a 2000-meter swim into four sessions—breaking it up makes it feel more achievable. Finally, I also provide a PDF version of the notes, making it convenient to print and read at your own pace.

3. In academia, people often use abbreviation, especially in technical writing. While this can be convenient for the author, it’s not always beginner-friendly. In my notes, I’ll do my best to implement a hover-over feature for annotating abbreviation. When you hover over the cursor on the abbreviation for a second, its full term will appear on the screen. For example: move your cursor on IAT . BTW , if you see NE , it means “Not essential and you may skip”.

Full slides for course introduction

Click here

List of Lectures

• Lecture 1. Feature Extraction and PCA

• Lecture 2. Another Perspective on PCA

• Lecture 3. Artificial Neural Networks and Deep Learning