For background information on the topic as a whole, scroll to the end of this page.
Available Project Proposals
If you are interested in the general topic of Machine Learning, or if have your own project idea related to the topic, please contact us directly. Alternatively, you can also work on one of the following concrete project proposals:
Automatic detection and classification of scientific papers through the use of NLP (Yeray Barrios Fleitas, Faizan Ahmed) Supervisor: Yeray Barrios Fleitas, Faizan Ahmed
In research review papers,one of the most exciting contributions is usually the tables where the reviewed papers are classified according to a list of criteria related to the specific topic. This is an arduous reading jobfor the researcher, but in practice the type of information needed to classifythe paper is often in the methodology section. Attributes such as the type of paper (conceptual, experimental, exploratory, etc.), the size of the sample, or the analysedvariablesare the type of information used for classifyingthese papers.Could a softwarebe trained to read and classify papers according to their methodological nature?
In this project, your goal will be:
- Identify the catalysts that indicate necessary attributes for a generic classification of papers.
- Make use of natural language processing (NLP)techniqueson a single documentto correctly identify the type of study and the sample size)
- Apply it to the complete list of articles reviewed in a systematic review of the literature and export a table with the classified papers. Analysethe degree of precision achieved.As supervisors, we will provide you with the sources of knowledge and material necessary to write a research paper successfully.
If the student obtains significant results and is interested, s/he may consider publishing their results in aconference or journal.
Point cloud classification, segmentation, and visulization (Faizan Ahmed) Supervisors: Faizan Ahmed
Point clouds are sets of spatial data points captured by 3D scanning techniques such as lidar. These point clouds contain many million points of data, resulting in 3D representations of the railway environment. Point cloud data can be used to create machine learning models that can classify the object in rail infrastructure automatically.
The main research question is: How to reliably segment, classify, and visualize point clouds for railway catenary systems with scant computational power, memory, and ground truth labels?
However, the student can focus on a (combination of) topic(s) given below:
- create an end-to-end pipeline for point cloud visualization using existing libraries
- visualizing model output(or intermediate processing) to explore interpretability/explainability
- application of XAI techniques to point clouds
- leverage existing trained models for point cloud labeling
- pre-process (down-sampling. Augmentation, filtering, voxelization etc.) point clouds for accurate model training
- train deep learning models for point cloud segmentation and object detection
Some previous assignments related to this topic:
Quantitative analysis of the diversity index in student teams (Yeray Barrios Fleitas) Supervisor: Yeray Barrios Fleitas
How can we quantitatively compare two different teams? We first need to know what we are comparing it to. Suppose we want to know how diverse a specific team is in terms of the gender of its members and that it can only be male or female. A highly diverse (heterogeneous) team has the same number of male and female members. A poorly diverse (homogeneous) team may consist of all male or all female students. In this case, we have two types of homogeneity, the one that has the masculine gender and the one that tends to the feminine gender. What statistic best suits this case to analyse a team's level of diversity? What happens if, instead of considering gender as a dichotomous variable (two values), we consider it polytomous (multiple values)? Answering these questions will be your primary goal in this project. At the beginning of the project, I will provide you with a list of variables on which you should analyse the level of diversity and research material based on actual data obtained during module 4: Data & Information. Your first mission will be to review the existing literature to catalogue the strategies that have been followed so far. Then, you must apply a subset of these statistics on a real sample and analyse the effectiveness of the results. As a supervisor, we will provide you with the sources of knowledge and material necessary for you to successfully write a research paper.
This paper should contain:
- A review of the most used statistics to measure diversity in student teams in the shape of a taxonomy
- The performance analysis you did with the dataset
- A list of recommendations for comparing teams based on their different attributes.
If the student is interested and the scope of the project allows it, a web application can be developed that reads a spreadsheet with information about the students and the teams they belong to as input for generating a diverse-level report as output.
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Background
Machine learning helps to learn from enormous amounts of data gathered as a direct or indirect product of a process, whether it be an industrial product line, efficient farming for food production, prediction of bacterial growth, or any other industrial, healthcare-related, or technological endeavor. Similar to humans, machine learning extracts patterns from data if enough examples are provided. Reinforcement learning is an action-oriented type of learning in which an agent tries to maximise a given reward signal, without being told explicitly which actions to take. This type of learning is often combined with other types of machine learning. For instance, deep learning is widely applied in scenarios with large state spaces.
The topic of machine learning leads to projects of varying nature such as theoretical (algorithm development, computational efficiency, and convergence of machine learning algorithm, formal representation), algorithm training for classification or regression (see some example projects below), predictive maintenance, and reinforcement learning.
Related Modules
- Intelligent interaction design