{"id":188,"date":"2015-09-16T16:23:29","date_gmt":"2015-09-16T16:23:29","guid":{"rendered":"http:\/\/project.dke.maastrichtuniversity.nl\/RAI\/?page_id=188"},"modified":"2016-12-11T14:50:41","modified_gmt":"2016-12-11T14:50:41","slug":"thesesinternships-available-for-students","status":"publish","type":"page","link":"https:\/\/project.dke.maastrichtuniversity.nl\/RAI\/?page_id=188","title":{"rendered":"Available Theses-Internships"},"content":{"rendered":"
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Proposed Bachelor\/Master Thesis (contact Gerasimos (Jerry) Spanakis<\/a>\u00a0or Gerhard Weiss)<\/a>:<\/strong><\/p>\n

Machine learning (especially deep learning techniques) have recently found their way to mimic existing artistic styles<\/a> or judging pictures, paintings for their aesthetic quality<\/a>. While the inner details of the algorithms are not directly interpretable, a question that arises is whether the algorithms learn something about concepts like \u201charmony\u201d.<\/p>\n

In this thesis you will work with a real artist (that will provide us with enough data) and explore (a) if there is a way to build such a system that will identify whether an artistic synthesis is in harmony or not and (b) whether after having \u201cseen\u201d many examples the system is able to generate new syntheses that are in harmony. We are particularly interested in assessing the characteristics that lead the algorithm to make specific decisions. Symbolic learning (feature extraction, etc.) and deep learning (feature discovery, etc.) techniques will be explored.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Proposed Bachelor\/Master Thesis (contact Gerasimos (Jerry) Spanakis<\/a>)<\/a>:<\/strong><\/p>\n

Dialogue systems are emerging as a hot application area<\/a> of deep learning architectures. There are many examples of how deep learning can be applied to either general domain question-answering or to specific domains (e.g. the ubuntu corpus, company customer support). The main goal is that neural network has to model questions & answers and also keep track of the dialogue state. There is already a developed system (based on the VHRED architecture<\/a>) that for now provides answers based on information retrieval techniques (i.e. the answer that best matches the question).<\/p>\n

Your goal in this thesis would be to extend current model by fine-tuning its architecture and give the possibility to the \"chatbot\" to answer specific questions. There is also the opportunity of working with a specific corpus of customer support dialogues.\u00a0<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Proposed Bachelor\/Master Thesis (contact Gerasimos (Jerry) Spanakis<\/a>)<\/a>:<\/strong><\/p>\n

Convolutional neural networks have been extensively and successfully studied and applied to image data. Convolution<\/a>\u00a0is a powerful technique from signal processing that in the concept of images is applied by siding a small kernel matrix over all possible positions in the image with the goal of identifying important features. However,\u00a0convolution (\u201coptical flow\u201d) can be applied to other data (e.g. temporal<\/a>) usually in 1-dimension and can show how different variables convolve over time.<\/p>\n

Given a multivariate (numeric and categorical) dataset spread over discrete time steps, your goal in this thesis would to be to study how 1-dimensional-convolution can be applied to a prediction task (research into how variables can be represented and how convolution windows can be applied) and construct a NN architecture that solves the problem.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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TRANSFER LEARNING IN SERIOUS GAMES<\/h3>\n
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Proposed Master (AI or OR)\u00a0Thesis (contact Dr\u00a0Stelios Asteriadis<\/a>):<\/strong><\/p>\n

Transfer learning is an important open issue in the field of machine learning. The exchange of knowledge allows for a better and faster training of new systems using \u201cexperiences\u201d acquired through other ones.<\/p>\n

We are looking for a student with good programming skills (preferably Python and\/or Java) interested in developing their master thesis in this promising and highly rising area in AI. The main objective of the project is to develop a general AI to be applied in data acquired through different sensors during interacting with serious games. The method to be implemented must allow for knowledge sharing between different games, in order to improve the overall experience provided by the interaction. Transfer learning will facilitate the training of a device which uses, for instance, video data for emotion elicitation, using the information acquired using a different device through, e.g., audio or inertial sensor data (how computer vision based emotion recognition can benefit emotion recognition from other sensors).<\/p>\n

The outcome of this project will be the implementation of a method to modify game parameters (e.g. difficulty level) to adapt the game to user performance (e.g. emotional state). This thesis will be focused on transfer learning and it won\u2019t start from scratch. Initially, some datasets and analysis algorithms will be provided in order to facilitate the implementation of the system.<\/p>\n

Key words<\/strong>: transfer learning, machine learning<\/p>\n

Level<\/strong>: Master in AI or OR. Good programming skills (preferably Python and\/or Java) and knowledge about machine learning<\/p>\n

Recommended literature<\/strong>:<\/p>\n

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  1. Zhu, Y. Chen, Z. Lu, S. Jialin P., G. R. Xue, Y. Yu, Q. Yang, Heterogeneous Transfer Learning for Image Classification, AAAI, 2011<\/li>\n
  2. T. Zhou, S. J. Pan, I. W. Tsang, Y. Yan, Hybrid Heterogeneous Transfer Learning through Deep Learning, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence<\/li>\n<\/ol>\n<\/div>\n<\/div><\/div><\/div><\/div>
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    AFFECTIVE STATE RECOGNITION THROUGH MOBILE PHONE USE<\/h3>\n
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    Proposed Bachelor or Master (AI or OR) Thesis (contact Dr\u00a0Enrique Hortal<\/a>):<\/strong><\/p>\n

    Mobile phones are probably the first device you use every morning and the last one you use before going to bed. It is becoming a \u201cmandatory\u201d gadget, as it can provide us with a huge amount of information from different sensors. The objectives of this project are the following:<\/p>\n