Advanced Data Analysis with Machine Learning
Course: Advanced Data Analysis with Machine Learning
Code: 3ФЕИТ07008
ECTS points: 6 ECTS
Number of classes per week: 3+0+0+3
Lecturer: Asst. Prof. Dr. Hristijan Djoreski
Course Goals (acquired competencies): Analysis of structured and unstructured data. Working with algorithms of Artificial Intelligence, Machine Learning, and Deep Learning.
Course Syllabus: Advanced Data analysis of different types of data, including: structured and unstructured data, time series, pictures, sound, and similar. Processing and analysis of the data using Artificial Intelligence, Machine Learning and Deep Learning methods. Application of these methods to analyse the data: pre-processing of data (filtering), attribute extraction, building classification and regression models, clustering, visualization of data and models, comparison of models. Implementation of the methods using Java or Python development environment (Weka, sklearn, tflearn, numpy, matplotlib, pandas, TensorFlow, Keras, Pythorch).
Literature:
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Required Literature |
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No. |
Author |
Title |
Publisher |
Year |
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1 |
Ordóñez, F.J.; Roggen, D |
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition |
Sensors 2016 |
2016 |
|
2 |
Vyas, N.; Farringdon, J.; Andre, D.; Stivoric, J.I. |
Machinelearning and sensor fusion for estimating continuous energy expenditure |
In Proceedings of the Innovative Applications of Artificial Intelligence Conference |
2011 |
|
3 |
Hammerla NY, Halloran S, Plötz T. |
Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables |
IJCAI 2016 |
2016 |
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Additional Literature |
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No. |
Author |
Title |
Publisher |
Year |
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1 |
Chavarriaga, R.; Sagha, H.; Calatroni, A.; Digumarti, S.; Tröster, G.; Millán, J.; Roggen, D. |
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition |
Pattern Recognit. Lett. 2013 |
2013 |
|
2 |
Yu Guan, Thomas Ploetz |
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables |
Ubicomp 2017 |
2017 |
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3 |
Sebastian Munzner, Philip Schmidt, Attila Reiss, Michael Hanselmann, Rainer Stiefelhagen, Robert Durichen |
CNN-based Sensor Fusion Techniques for Multimodal Human Activity Recognition |
Ubicomp 2017 |
2017 |
