| CODE | ARI3214 | ||||||
| TITLE | Introduction to Deep Learning | ||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||
| MQF LEVEL | 6 | ||||||
| ECTS CREDITS | 5 | ||||||
| DEPARTMENT | Artificial Intelligence | ||||||
| DESCRIPTION | Deep Learning systems, or deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, speech and image processing to autonomous driving. It is a type of Machine Learning technique that enables computer systems to improve with experience and data, whilst at the same time offering great power and flexibility. The study unit will expose students to the basic concepts of deep learning and their applications in various AI tasks. It will also cover the most established deep learning algorithms and architectures, and analyse the different scenarios they are used in. Study-Unit Aims: - Introduce students to deep learning; - Introduce the basic theoretical foundations of deep learning; - Help students understand the different building blocks of a deep learning architecture; - Enable the student to select the most appropriate architecture and/or library/framework to solve a machine learning problem; - Prepare students for other study-units and/or dissertations that require knowledge of deep learning. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Analyse the different building blocks of a deep learning system; - Demonstrate an understanding of how the building blocks work; - Comprehend the generic components of a deep learning architecture; - Given a machine learning problem, decide what type of deep learning architecture to use to solve that problem. 2. Skills: By the end of the study-unit the student will be able to: - Discuss and critically analyse different deep learning architectures; - Identify scenarios where the different types of techniques are best suited; - Implement their own deep learning system using available libraries and frameworks. Main Text/s and any supplementary readings: Main Texts: - Michael Nielsen. Deep Learning 2017. available online: http://neuralnetworksanddeeplearning.com Supplementary Readings: - Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016. Available online: http://www.deeplearningbook.org/ |
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| STUDY-UNIT TYPE | Lecture, Independent Study and Project | ||||||
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| LECTURER/S | Marc Tanti |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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