SDS 418: Deep Learning Approaches
Course Title |
Deep Learning Approaches |
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Course Code |
SDS 418 |
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Course Type |
Elective |
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Level |
Master’s |
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Year / Semester |
1st / 2nd (subject to change) |
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Instructor’s Name |
Mihalis Nicolaou |
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ECTS |
10 |
Lectures / week |
1 (2h) |
Laboratories / week |
1 (1h) |
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Course Purpose and Objectives |
To teach students the theoretical concepts on deep learning and how to implement and use them to automatically extract features from data and build prediction models for several applications. Methods covered include feedforward, convolutional, recurrent and recursive networks. |
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Learning Outcomes |
Students will learn the fundamentals, implement and use deep learning methods for a wide range of applications such as object recognition in images, anomaly detection, pattern recognition in omics (e.g., genomics) data, medical diagnosis, etc. |
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Prerequisites |
None |
Requirements | - | ||||
Course Content |
Neural networks: feedforward networks, radial basis function networks, recurrent neural networks, modular neural networks. Usage and hands on experience of state of the art libraries for Deep Learning. |
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Teaching Methodology |
Lectures, exercises, seminars, reports |
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Bibliography |
1) I. Goodfellow, Y. Bengio A. Courville, “Deep Learning”, MIT press. 2) Sebastian Raschka, David Julian, John Hearty, “Python: Deeper Insights into Machine Learning”, Packt Publishing. |
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Assessment |
25% coursework, 75% exam |
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Language |
English |