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Module Code - Title:

CE4031 - INTRODUCTION TO DEEP LEARNING AND FRAMEWORKS

Year Last Offered:

2021/2

Hours Per Week:

Lecture

2

Lab

0

Tutorial

1

Other

3

Private

4

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

To give students an insight into Deep Learning and associated Frameworks and prepare them to take more advanced Artificial Intelligence modules.

Syllabus:

1. Fundamentals and basic concepts of deep learning and related machine learning 2. Programming basics for deep learning 3. Introduction to deep learning frameworks (e.g. TensorFlow, PyTorch, Caffe2, CNTK etc. ) 4. Deep learning platforms and acceleration 5. Applications of deep learning (e.g. image classification, signal processing, natural language processing etc)

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

1. Given information and instruction, the student will gain insight and understand the key components in machine learning and deep learning systems. 2. Given representative problems, the student will be able to identify use-cases for machine learning and deep learning 3. Given information on prominent deep learning frameworks, the student will understand and compare their core features and usability. 4. Given a relevant cloud hosted platform, the student will develop the ability to use the supported deep learning frameworks. 5. Given problems to investigate, the student will implement, analyse and present outputs from deep learning frameworks. 6. Given large and real-world data sets for deep neural networks, the student will develop the ability to process and analyse the data. 7. Given selected practical problems, the student will have the ability to identify, develop and implement appropriate deep learning solutions.

Affective (Attitudes and Values)

1. Given problems and data to investigate, the student will identify and discuss any significant ethical issues such as privacy, confidentiality, ownership, transparency and identity. 2. Given datasets, the student will question and demonstrate whether the data is representative and identify potential biases. 3. Following exposure to various frameworks and hosted platforms, the student will judge and challenge the limitations of current deep learning techniques.

Psychomotor (Physical Skills)

N/A

How the Module will be Taught and what will be the Learning Experiences of the Students:

The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials.

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Goodfellow & Bengio (2016) Deep Learning , MIT Press

Other Relevant Texts:

Chollet (2017) Deep Learning with Python , Manning Publications
Kim (2017) MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence , Apress
Subramanian (2017) Deep Leaning with PyTorch , Packt Publishing
Langtangen (2016) A Primer on Scientific Programming with Python , Springer
Beazley (2009) Python Essential Reference , O'Reilly
Marsland (2014) Machine Learning: An Algorithmic Perspective , CRC Press

Programme(s) in which this Module is Offered:

Semester - Year to be First Offered:

Module Leader:

pepijn.vandeven@ul.ie