Materials for Applied Data Science profile course INFOMDA2 Battling the curse of dimensionality.
The ever-growing influx of data allows us to develop, interpret and apply an increasing set of learning techniques. However, with this increase in data comes a challenge: how to make sense of the data and identify the components that really matter in our modeling efforts. This course gives a detailed and modern overview of statistical learning with a specific focus on high-dimensional data.
In this course we emphasize the tools that are useful in solving and interpreting modern-day analysis problems. Many of these tools are essential building blocks that are often encountered in statistical learning. We also consider the state-of-the-art in handling machine learning problems. We will not only discuss the theoretical underpinnings of supervised learning, but focus also on the skills and experience to rapidly apply these techniques to new problems.
During this course, participants will actively learn how to apply the main statistical methods in data analysis and how to use machine learning algorithms and visualization techniques, especially on high-dimensional data problems. The course has a strongly practical, hands-on focus: rather than focusing on the mathematics and background of the discussed techniques, you will gain hands-on experience in using them on real data during the course and interpreting the results.
The course INFOMDA1 (or equivalent) serves as a sufficient entry requirement for this course. For information about the contents of the INFOMDA1 course, refer to its course website.
At the end of this course, students are able to apply and interpret the theories, principles, methods and techniques related to contemporary data science and understand and explain different approaches to data analysis:
Freely available sections from the following books:
In this course, we will exclusively use R & RStudio for data analysis.
First, install the latest version of R for your system (see
https://cran.r-project.org/
). Then,
install the latest (desktop open source) version of the RStudio
integrated development environment
(link
).
We will make extensive use of the tidyverse
suite of packages, which
can be installed from within R
using the command
install.packages("tidyverse")
.
To develop the necessary skills for completing the assignments and
the exam, 9 R
practicals must be made. These exercises are not
graded, but students must fulfill them to pass the course.
There are two pass/fail assignments. Successful and timely completion of these assignments will grant you a bonus point on the exam.
100% of your grade will be determined by an exam featuring both
knowledge questions as well as practical data analysis skills in
R
. Some example questions will be made available to you so you can
prepare.
Day | Date | Time | Location | Description |
---|---|---|---|---|
Wednesday | 17-11-2021 | 13:15 - 15:00 | BBG 161 | Lecture 1 |
Friday | 19-11-2021 | 13:15 - 15:00 | BBG 201 | Q&A 1 |
Wednesday | 24-11-2021 | 13:15 - 15:00 | BBG 161 | Lecture 2 |
Friday | 26-11-2021 | 13:15 - 15:00 | BBG 201 | Q&A 2 |
Wednesday | 01-12-2021 | 13:15 - 15:00 | BBG 161 | Lecture 3 |
Friday | 03-12-2021 | 13:15 | Deadline assignment 1 | |
Friday | 03-12-2021 | 13:15 - 15:00 | BBG 201 | Q&A 3 |
Wednesday | 08-12-2021 | 13:15 - 15:00 | BBG 161 | Lecture 4 |
Friday | 10-12-2021 | 13:15 - 15:00 | BBG 201 | Q&A 4 |
Wednesday | 15-12-2021 | 13:15 - 15:00 | BBG 161 | Lecture 5 |
Friday | 17-12-2021 | 13:15 - 15:00 | BBG 201 | Q&A 5 |
Wednesday | 22-12-2021 | 13:15 - 15:00 | BBG 161 | Lecture 6 |
Friday | 24-12-2021 | 13:15 - 15:00 | BBG 201 | Q&A 6 |
Break | ||||
Wednesday | 12-01-2022 | 13:15 - 15:00 | BBG 161 | Lecture 7 |
Friday | 14-01-2022 | 13:15 - 15:00 | BBG 201 | Q&A 7 |
Wednesday | 19-01-2022 | 13:15 - 15:00 | BBG 161 | Lecture 8 |
Friday | 21-01-2022 | 13:15 | Deadline assignment 2 | |
Friday | 21-01-2022 | 13:15 - 15:00 | BBG 201 | Q&A 8 |
Wednesday | 26-01-2022 | 13:15 - 15:00 | BBG 161 | Lecture 9 |
Friday | 28-01-2022 | 13:15 - 15:00 | BBG 201 | Q&A 9 |
Friday | 04-02-2022 | 08:30 - 11:30 | Megaron | Exam |
Friday | 04-03-2022 | TBD | Resit |
17-11-2021 | 13:15 - 15:00
Refresh your memory:
Assignments 1-5 of the first practical.
24-11-2021 | 13:15 - 15:00
Take-home exercises of the practical.
01-12-2021 | 13:15 - 15:00
Take-home exercises of the practical.
Partial least squares
(link).
Hand in on blackboard before practical 3 (03-12-2021 | 13:15
).
08-12-2021 | 13:15 - 15:00
Take-home exercises of the practical.
15-12-2020 | 13:15 - 15:00
Take-home exercises of the practical.
22-12-2020 | 13:15 - 15:00
Take-home exercises of the practical.
12-01-2021 | 13:15 - 15:00
TBD
19-01-2021 | 13:15 - 15:00
TBD
TBD. Hand in on blackboard before practical 8
(21-01-2022 | 13:15
).
26-01-2021 | 13:15 - 15:00
TBD
04-02-2021 | 8:30 - 11:30
Target date: 05-03-2021
, to be confirmed.