INFOMDA2

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Materials for Applied Data Science profile course INFOMDA2 Battling the curse of dimensionality.

Course Description

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 different techniques, but focus also on the skills and experience needed 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.

Prerequisites

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.

Course Objectives

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 to understand and explain different approaches to data analysis:

Required Readings

Freely available sections from the following books:

Required Software

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").

Course Policy

Weekly course flow

In-person course policy

Grading policy

Class Schedule

You can find the up-to-date class schedule with locations on mytimetable.uu.nl.

Key dates and deadlines

Day Date Time Location Description
Wednesday 16-11-2022 13:15 - 15:00 BBG 223 Lecture 1
Friday 18-11-2022 11:00 - 12:45 BST LIE Lab 1
Wednesday 23-11-2022 13:15 - 15:00 BBG 223 Lecture 2
Friday 25-11-2022 11:00 - 12:45 BBG 209 Lab 2
Wednesday 30-11-2022 13:15 - 15:00 BBG 223 Lecture 3
Friday 02-12-2022 11:00   Deadline A1
Friday 02-12-2022 11:00 - 12:45 BBG 209 Lab 3
Wednesday 07-12-2022 13:15 - 15:00 BBG 223 Lecture 4
Friday 09-12-2022 11:00 - 12:45 BBG 209 Lab 4
Wednesday 14-12-2022 13:15 - 15:00 BBG 223 Lecture 5
Friday 16-12-2022 11:00 - 12:45 BBG 209 Lab 5
Wednesday 21-12-2022 13:15 - 15:00 BBG 203 Lecture 6
Friday 23-12-2022 11:00 - 12:45 BBG 209 Lab 6
Break        
Wednesday 11-01-2023 13:15 - 15:00 BBG 223 Lecture 7
Friday 13-01-2023 11:00 - 12:45 BBG 209 Lab 7
Wednesday 18-01-2023 13:15 - 15:00 BBG 223 Lecture 8
Friday 20-01-2023 11:00   Deadline A2
Friday 20-01-2023 11:00 - 12:45 BBG 209 Lab 8
Wednesday 25-01-2023 13:15 - 15:00 BBG 223 Lecture 9
Friday 27-01-2023 11:00 - 12:45 BBG 209 Lab 9
Friday 03-02-2023 14:00 - 16:00 BBG 201 Exam
Friday 03-03-2023 TBD TBD Resit

Lecture 1: Introduction & betting on sparsity with the LASSO

Required reading

Optional reading

Lecture 2: Dimension reduction 1

Required reading

Lecture 3: Dimension reduction 2

Required reading

Assignment 1

Partial least squares (link). Hand in on blackboard before practical 3 (02-12-2022 @ 11:00).

Lecture 4: Deep learning

Required reading

Lecture 5: Clustering

Required reading

Optional reading

Lecture 6: Model-based clustering

Required reading

Optional reading

Winter break

Lecture 7: Time series

Required reading

Lecture 8: Text mining 1

Required reading

Assignment 2

Comparing clustering methods (link). Hand in on blackboard before practical 8 (20-01-2023 @ 11:00).

Lecture 9: Text mining 2

Required reading

Exam

03-02-2023 | 14:00 - 16:00

Resit

Target date: 03-03-2023, to be confirmed.