In customer and market analysis, the increasing amount of data available creates an immense opportunity for machine learning. This course introduces a small starter-set of simple (yet powerful) machine learning methods including their theoretical background, with practical hands-on exercises. The R programming language is used for the exercises and homeworks: prior programming experience is not required, but is recommended for successful passing of this course (see details below).

keywords: supervised and unsupervised learning, model performance evaluation, k-nearest neighbors (kNN), decision trees, random forest, k-means clustering, neural networks, support vector machines (SVM)

Our goal is to give you a set of tools you can use to make data-driven decisions wherever data is available, and hopefully inspire your future work in this exciting, dynamic field. Additionally, we aim to give an insight into some of the many modern applications of machine learning which are increasingly invading our private and professional lives. 

• Understand the theory behind a few basic machine learning methods.
• Get familiar with machine learning in R using real life examples.
• Learn to chose the right method for a given task and evaluate results.

You need to attend at least 80% of all classes to pass the course.

A typical class contains a lecture and practice part, during which we guide the students through a pre-prepared programming exercise. Students are asked to prepare homeworks where methods from class are applied onto a new problem. Group challenge is a "kaggle"-style competition in which small independent groups of students compete for some bonus points. 

The final grade will be evaluated as follows:

• Class participation: [weight: 10%]
• Homeworks (x5): [weight: 25%]
• Final exam: [weight: 65%]
• Group challenge: [5% bonus]

We ask a couple of random students to present the findings from the homework at the beginning of next lecture. Doing the homework is a good start to pass the final exam, which contains open ended and multiple choice questions, with topics covering the material from the lectures. The final focuses on understanding the methods and concepts, not programming skills or math.

To pass this course: 

• your weighted final grade needs to exceed 60%
• you need to submit at least 3 out of 5 homeworks
• you need at least 50% on the Final test

The class does not take place in a computer room.

Please bring your own laptop with a working RStudio installation. 

The software is free, runs on Windows, Mac, and Linux.

Download RStudio Desktop from: https://rstudio.com/products/rstudio/ 

Prior programming experience is highly recommended. For those who are not familiar with R, there are many online resources that provide the basic skills required to take this course, and we strongly encourage you to do so. The following is just a small sample of the vast resources available for free:

https://www.udemy.com/course/r-basics/

https://www.coursera.org/learn/r-programming

https://www.edx.org/course/data-science-r-basics

https://www.datacamp.com/courses/free-introduction-to-r

Consultations after prior arrangement via email.