Machine Learning |

Data Mining KSD (Autumn 2025)

Tue, 26 Aug 2025 00:00:00 +0000

Description

This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.

The course will cover the following main topics:

The data mining process
Cluster analysis
Data pre-processing
Pattern and association mining
Classification and prediction

Application examples will be given from domains including demographics, image processing and healthcare.

After the course, the student should be able to:

Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.
Implement basic pre-processing, association mining, classification and clustering algorithms.
Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.
Work efficiently in groups and evaluate the algorithms on real-world problems.

Staff

Course Manager and Teacher:
Teaching Assistant:
Teaching Assistant:

Supporting Materials

Data Mining KSD (Autumn 2024)

Fri, 30 Aug 2024 00:00:00 +0000

Description

The course will cover the following main topics:

The data mining process
Cluster analysis
Data pre-processing
Pattern and association mining
Classification and prediction

Application examples will be given from domains including demographics, image processing and healthcare.

After the course, the student should be able to:

Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.
Implement basic pre-processing, association mining, classification and clustering algorithms.
Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.
Work efficiently in groups and evaluate the algorithms on real-world problems.

Staff

Course Manager and Teacher:
Teaching Assistant:

Supporting Materials

Using Machine Learning to Identify Communal Worldwide Hand Gestures for Virtual and Hybrid Meetings Context

Sat, 25 Nov 2023 00:00:00 +0000

This project explores how machine learning can support a hand gesture vocabulary that promotes global standardization and inclusivity. It investigates hand gesture recognition technology that allows users to communicate and control devices using natural, intuitive hand movements without touching anything. This technology can enhance user experience, safety, hygiene, and accessibility, especially for companies with international employees.

The project has three main objectives:

Investigate how users from diverse backgrounds use hand gestures in virtual and hybrid meetings and which hand gestures they prefer for specific actions.
Train machine learning models to identify the most common and consistent hand gestures among cross-cultural users for controlling a given function in the interactive system or device.
Propose a universal hand gesture dictionary that can support a global standardization for new collaboration products and systems that use this technology and foster understanding and well-being among users who work with international teams.

Hand gesture recognition technology has huge potential in business meetings and collaboration products, as demand for meetings and e-learning is growing worldwide due to changes in work and study modes. Many industries are adopting this innovative resource, and some applications have already been launched, including the Zoom platform and certain collaborative business cameras. However, there is still room for improvement and innovation, as there is no shared standard vocabulary for hand gestures, and some gestures may have different or offensive meanings in different cultures. Therefore, it is important to consider the cultural significance of gestures and to create a conscious, communal vocabulary that is universally understood and accepted.

To create hand gesture recognition products that can be used by global users from diverse backgrounds, it is not enough to ensure a high recognition rate alone. These products also need to provide a positive user experience, avoiding any embarrassment, misunderstanding, or offense that may discourage users from using the technology. Therefore, there is a need for a standardized hand gesture vocabulary that can achieve universal understanding, inclusivity, and acceptability. By conducting cross-cultural user studies, a hand gesture vocabulary can be carefully constructed to suit the needs and preferences of users from different cultures. This can increase consumer confidence and the market potential of the products, as well as improve the state of the art in hand gesture recognition for virtual and hybrid meeting contexts.

This project is conducted by an Industrial Ph.D. student, , who has received a grant of DKK 2.0 million from and . Elizabete works with the Video Technology team at , a leading company in the collaboration business products, and studies at the , a renowned institution for research and education in information technology.

Designing and Implementing Voyager - An Intelligent Travel Companion

Fri, 26 May 2023 00:00:00 +0000

Abstract

This paper explores the development of a user-friendly mobile application that combines travel planning and itinerary management. By comparing existing apps on the market and conducting user research, we identify user needs and ways to differentiate our app from competitors. Our app incorporates state-of-the-art AI technology to generate personalized itineraries for users visiting Copenhagen. We present the results of user involvement, including user interviews and usability tests, and analyze their feedback. In addition, we compare similar apps on the market and discuss design choices for our app’s user interface. We provide detailed information on the technical implementation of our app and explore future possibilities for development and integration with AI. Overall, this paper provides insights into the creation of a travel planning and itinerary management app that offers a unique and user-friendly experience for travelers. Our approach combines user research, innovative technology, and thoughtful design choices to create an app that stands out in a competitive market.

Retrieval, Visualization, and Analysis of Graffiti in Copenhagen

Fri, 26 May 2023 00:00:00 +0000

Abstract

This project investigates the relationship between the occurrence of graffiti and various factors in the Amager districts of Copenhagen. The research is focused on social factors such as population density and income groups. However, the relation to crime and areas with graffiti is also examined. Through geospatial analysis and machine learning models, the project explores patterns and correlations associated with graffiti. The geospatial analysis showed that certain areas have a higher concentration of graffiti, with urban areas exhibiting more compared to residential areas. The machine learning models showed limited success in predicting the occurrence of graffiti solely based on income and population density but achieved moderate accuracy in identifying graffiti tags. The findings suggest that factors beyond income and population density may contribute to graffiti occurrence. Further research is needed to explore additional factors and improve the predictive models. Overall, this project provides valuable insights into the distribution and potential influencing factors of graffiti, contributing to a better understanding of this urban phenomenon.

Correct Execution of Weightlifting Exercises using Pose Estimation

Wed, 11 Jan 2023 00:00:00 +0000

Abstract

This thesis is a technical analysis of the weightlifting exercise deadlift, using pose detection and machine learning. Weightlifting is an increasingly popular exercise method with substantial benefits. However, if done incorrectly could lead to injuries. The project aims to research whether machine learning technologies help create a solution that is an alternative to hiring a personal trainer. The technical goal is to recognize correct and incorrect movements from video input by running videos through Google MediaPipe pose detection to gather x, y, and z coordinates. The dataset contains either correct or incorrect video labels to feed the machine learning prediction model to predict whether or not a particular video was correct or incorrect. By doing this, the trained model can clear distinct between correct and incorrect movement, resulting in an alternative to hiring a personal trainer.

Development of App to the Restaurant Business

Wed, 24 Aug 2022 00:00:00 +0000

Abstract

This project researches whether Machine Learning (ML) and Social Media can help improve the experience for a customer who dines at restaurants. Previous studies have shown that restaurants should make their reservation and payment systems digital and online to increase customer service. Our project focus on the User Experience of the restaurant experience. We found out that ML and Social Media can contribute positively to the experience by focusing on the user-to-restaurant interaction rather than the user-to-user interaction. The limited user-to-user elements should focus on restaurant reviews and planning events to which ML can contribute positively, by identifying food images and connecting them to menu items on a menu card. This project discusses various ML and Social Media elements and how to utilize them, relying on data collected through questionnaires and interviews. Though marketing for businesses is not within this project’s scope, the test group mentioned that they would rather receive offers and news from restaurants than reading user updates, which introduces an exciting angle to how restaurants could use the proposed system further. The business aspects still need research, exploring how restaurants can use the proposed system for marketing themselves and how beneficial ML would be for business owners.

Correct Disc Golf Form: Classification of the Backhand Throw using Neural Networks

Fri, 24 Jun 2022 00:00:00 +0000

Abstract

Form is essential when analyzing and reviewing a backhand disc golf throw. The form defines if the throw is performed correctly and the poses of the body define the form. By looking at the body poses the throw can be classified, critiqued, and improved upon. The form consists of different motions which are analyzed using 3D data collected using machine learning solutions on a data set of recorded disc golf throws. By processing the 3D data from recorded throws the form is classified into three classes that represent the start, mid, and end of the throw. The three classes are shown as clusters using Principal Component Analysis (PCA). The PCA showed more overlapping clusters for the start and middle of the throw compared to the end. Classification solutions include a variation of trained LSTM networks and a solution using MediaPipe Pose Classification. The paper concludes that LSTM models perform faster and more accurately than the solution using MediaPipe Pose Classification when analyzing disc golf throws. However, the classification only provides insight for classifying the different forms and not the quality of form.

Using Machine Learning to Improve the Whiteboard Experience

Thu, 23 Jun 2022 00:00:00 +0000

Abstract

Virtual meetings and conferences are getting more common and more mainstream in the workplace. This shift in use of technology means that other work practices has to adapt to work with virtual meetings. One such practice is the use of writing on whiteboards. Just like some people prefer to read from a book instead of a monitor, a practice like writing on a whiteboard might never be replaced by writing on a tablet. This creates a set of problems of how can the whiteboard be integrated to work with the virtual world. There’s many ideas and potential solutions for this like using text recognition, but most of these solutions require the whiteboard to be detected in the first place. To solve this issue, a whiteboard detection model is proposed which is composed of a convolutional neural net to classify whiteboards in real-time videos through semantic image segmentation and computer vision to process the outline of the classified whiteboards into a set of points which can be used for further analysis and processing.

Object Tracking System (VidIT)

Fri, 04 Jun 2021 00:00:00 +0000

Abstract

This thesis investigates, if an IT product can increase learning in an online setting. Information is included in regards of learning and the development of VidIT, which is an automated tracking system powered by a smartphone and an Arduino. The system can track people with the help of a motorized pan tilt mount. The purpose of VidIT is to enhance learning during COVID-19, by enabling students and teachers to record themselves single-handily while moving around. A survey, a user test and a performance test was conducted to gather data on the current situation of teaching in an online setting, testing of the usability and performance of VidIT. Based on the tests, it was concluded that the resulting system worked as intended. However, some improvements are needed to effectively improve learning and teaching in an online setting. These improvements includes but are not limited to, streaming functionality, movement prediction and faster computation in relation to the objection detection algorithm.

Machine Learning in Android Applications

Fri, 29 May 2020 00:00:00 +0000

Abstract

This project is motivated by the ever-increasing popularity of machine learning techniques for solving repetitive everyday tasks, as well as the availability and importance of smartphones in today’s society. The combination of the two creates an environment in which the use of machine learning for simplifying mundane tasks in mobile applications may be experimented with. This project is a study in the use of machine learning in the context of such a mobile application, and specifically uses the Firebase ML Kit mobile SDK in that pursuit. The project includes the development of an application that allows users to generate descriptions of electronic devices they wish to post for sale on online marketplaces. The application utilizes machine learning and natural language generation to present the user with a textual description of the image submitted to the application. This project gives an overview of central machine learning principles, and goes into detail about the concepts relevant to solving the problem in question, namely classification, and neural networks. It also describes the process of implementing the application and how Firebase ML Kit provides machine learning capabilities, as well as how SimpleNLG provides natural language generation functionality to the application. The project further reflects on the application created and the use of ML Kit therein.

Road Safety with Android Auto and Machine Learning

Tue, 26 May 2020 00:00:00 +0000

Abstract

This thesis aims to research the question of how to predict road safety and how a driver can safely receive relevant information on road safety during a drive. This has become a relevant field of research, with sophisticated computing hardware available as a feature in cars. Additionally, operation areas and computation capability of mobile devices are expanding. The results of the experiment in this thesis has been an Android application which implements Machine Learning Models and Statistical Models to predict accidents, based on the current situation of the user. The Machine Learning Models do not provide valid scientific evidence for the predictions to be correct, due to the supervised historical traffic data, used to train the Machine Learning models, having inconsistent patterns of how accidents happen. The Machine Learning models are activated by Statistical Models using historical traffic data. The models are only compatible to some extent. This is limited by a historical weather data set, which only enables the model to predict accidents within a range incorrect with a level of abstraction. Thus the Statistical Models and the Machine Learning Models are implemented in the application using the Android System compatible with the Android Auto subsystem. Android Auto enables a safe communication channel with the drive. The application is distributable to Android Users and compatible with 60.3% of all android devices. In the future the models predictions might be invalid, as the behaviour of a car might change. Although the experiment does not provide any sophisticated pipeline for extending the models with new data.

Introduction to Image Analysis and Machine Learning, BSc and MSc (Spring 2018)

Sun, 28 Jan 2018 00:00:00 +0000

Description

The course is an introductory course to the basics of computer vision and machine learning. The objectives of this course are to provide students with the fundamental knowledge and skills required to design, build, and evolve smaller computer vision (CV) and machine learning programs.

The course introduces specific techniques from 2D and 3D image analysis and recent techniques from machine learning (e.g. deep learning / neural nets, SVM) to solve computer vision problems. The course provides an introduction to fundamentals of image formation, camera imaging geometry, point processing, color spaces, feature detection and matching, multiview geometry, motion estimation /tracking, and object detection/ recognition.

Computer Vision is the study of enabling machines to see and interpret the visual world through images and videos. Computer vision/image analysis and machine learning have in recent years played decisive roles in the development of new innovative applications based on images (e.g. various services provided by Google, Facebook, Microsoft, Snapchat etc). Having knowledge within computer vision and machine learning (e.g. deep learning) is an important skill needed in many modern innovative businesses and is likely to become even more important in the near future.

Many successful and robust computer vision techniques (such as object recognition, tracking) heavily rely on concepts from signal analysis and machine learning. The course will introduce specific machine learning techniques and apply them to relevant computer vision problems such as recognition, matching and search.

Through the course the student should be able to use the technique in their own applications and within more advanced topics on computer vision, data science and pervasive computing.

The course gives an introduction to computer vision, image analysis, linear algebra and machine learning. In the course we will present the fundamental models used for CV and machine learning as well as techniques to implement them. You will in the exercises and mandatory assignments be getting hands-on experience with the techniques described during the lectures. In the exercises you will be using Python and image analysis / machine learning packages such as OpenCV, Tensorflow/ Keras, Numpy. In the exercises we will use images from digital cameras and web cameras to illustrate the theory. Cameras needed for the exercises and assignments will be available to the students during the course.

In particular the course covers:

Pixel-based and local processing of images (smoothing, edges, conversion between color spaces) and color image processing.
Regression, classification, recognition
Machine learning techniques such as deep learning, neural networks, support vector machines applied to computer vision problems
Geometric transformations (2D and 3D) and linear algebra
Cameras, Stereo, structured light (Kinnect).
Python, OpenCV, Numpy,Tensorflow/Keras

After completing this course, the students should be able to:

Define, describe and relate concepts and mechanisms underpinning computer vision (CV) and machine learning (ML) methods and how they are related.
Analyze and explain key aspects of building medium-sized computer vision applications.
Explain, design and implement medium-sized interactive computer vision applications using concepts from ML.
Evaluate, select and adapt appropriate computer vision and machine learning techniques by applying the theoretical concepts and practical techniques from the course.
Clearly explain and employ basic linear algebra for computer vision and machine learning.
Apply the theory and implement rudimentary research papers within CV and ML as expected on a bachelor level.
Apply the theory and implement rudimentary research papers within CV and ML as expected on a master level.

Staff

Course Manager and Teacher:
Teacher:
Teaching Assistant:

Supporting Materials

Introduction to Image Analysis and Machine Learning, BSc and MSc (Spring 2017)

Wed, 01 Feb 2017 00:00:00 +0000

Description

To this end the course introduces specific techniques from 2D and 3D image analysis and recent techniques from machine learning to solve computer vision problems. The course provides an introduction to fundamentals of image formation, camera imaging geometry, point processing, color spaces, feature detection and matching, multiview geometry, motion estimation /tracking, and object detection/ recognition. Many successful and robust computer vision techniques (such as object recognition, tracking) heavily rely on concepts from signal analysis and machine learning. The course will introduce specific machine learning techniques and apply them to relevant computer vision problems such as recognition, matching and search.

Through the course the student should be able to use the technique in their own applications and within more advanced topics on computer vision, data science and pervasive computing.

The course gives an introduction to computer vision, image analysis, linear algebra and programming. In the course we will present the fundamental models used for CV and machine learning as well as techniques to implement them. You will in the exercises and mandatory assignments be getting hands-on experience with the techniques described during the lectures. In the exercises we will use images from digital cameras and web cameras to illustrate the theory. Cameras will be available during the course.

In particular the course covers:

Pixel-based and local processing of images (smoothing, edges, conversion between color spaces) and color image processing.
Segmentation
Object recognition.
Geometric transformations (2D and 3D)
Cameras, Stereo, structured light (Kinnect).
Machine learning techniques applied to computer vision problems such as regression, classification techniques and will touch upon deep learning.

After completing this course, the students should be able to:

Define, describe and relate concepts and mechanisms underpinning computer vision (CV) and machine learning (ML) methods and how they are related.
Analyze and explain key aspects of building medium-sized computer vision applications.
Explain, design and implement medium-sized interactive computer vision applications using concepts from ML.
Evaluate, select and adapt appropriate computer vision and machine learning techniques by applying the theoretical concepts and practical techniques from the course.
Clearly explain and employ basic linear algebra for computer vision and machine learning.
Apply the theory and implement rudimentary research papers within CV and ML as expected on a bachelor level.
Apply the theory and implement rudimentary research papers within CV and ML as expected on a master level.

Staff

Course Manager and Teacher:
Teaching Assistant:
Teaching Assistant:

Supporting Materials

Computer Vision til EvoBotten

Mon, 13 Jun 2016 00:00:00 +0000

Abstract

Automated production using robots play a significant role in chemistry, biotechnology and microbiology. Robots that are designed to perform a specific task are in the long run cheaper and much more efficient than humans. In research, however, most tasks are done by humans even though many tasks are cumbersome and repetitive. What complicates the introduction of robots in research are the small variations in the task sequences that are frequently introduced. In this Master thesis we will contribute to a project called the EvoBot which seeks to make robots an integral part of research in order to lower costs and speed up the process of experimenting. We develop a proof-of-concept for a computer vision application for the EvoBot that enables it to find, locate and classify petri dishes and well plates. We present the design of the system implemented using the OpenCV framework along with physical modifications to the EvoBot. In addition to the vision system we develop a framework to test its precision and accuracy and lay ground work for future improvements. We evaluate different approaches based on accuracy and precision results of the detection methods that are experimented with. The evaluation indicates that detection without the use of tagging is feasible for use in the industry, with the introduction of some future improvements.

Machine Learning |

Data Mining KSD (Autumn 2025)

Description

Staff

Supporting Materials

Data Mining KSD (Autumn 2024)

Description

Staff

Supporting Materials

Using Machine Learning to Identify Communal Worldwide Hand Gestures for Virtual and Hybrid Meetings Context

Designing and Implementing Voyager - An Intelligent Travel Companion

Abstract

Retrieval, Visualization, and Analysis of Graffiti in Copenhagen

Abstract

Correct Execution of Weightlifting Exercises using Pose Estimation

Abstract

Development of App to the Restaurant Business

Abstract

Correct Disc Golf Form: Classification of the Backhand Throw using Neural Networks

Abstract

Using Machine Learning to Improve the Whiteboard Experience

Abstract

Object Tracking System (VidIT)

Abstract

Machine Learning in Android Applications

Abstract

Road Safety with Android Auto and Machine Learning

Abstract

Introduction to Image Analysis and Machine Learning, BSc and MSc (Spring 2018)

Description

Contents

Staff

Supporting Materials

Introduction to Image Analysis and Machine Learning, BSc and MSc (Spring 2017)

Description

Contents

Staff

Supporting Materials

Computer Vision til EvoBotten

Abstract