ENGI48915: Deep Learning for Engineering

• None

• As stated in programme regulations.

• As stated in programme regulations.

• This module is designed solely for students studying Department of Engineering or MISCADA degree programmes.
• To provide a comprehensive understanding of deep learning techniques and their applications in engineering.
• To equip students with the knowledge and skills to design, implement, and evaluate deep learning models for engineering problems.
• To explore the integration of deep learning with engineering methods and domain knowledge.
• To foster critical thinking about the implications and limitations of deep learning in engineering contexts.

• Fundamentals of deep learning architectures and their applications in engineering.
• Advanced deep learning techniques for engineering problems, integrating e.g. physical models, simulations, discovery, and domain knowledge in engineering.
• Practical aspects of implementing, deploying, and scaling deep learning models in real-world engineering systems.

Subject-specific Knowledge:

• A comprehensive understanding of deep learning principles and architectures relevant to engineering applications.
• Knowledge of how deep learning can be integrated with physical models and engineering domain knowledge.

Subject-specific Skills:

• Ability to design and implement deep learning models for various engineering tasks.
• Proficiency in using deep learning frameworks and tools for engineering applications.
• Skills in data preparation, model training, and performance evaluation in engineering contexts.

Key Skills:

• Advanced problem-solving skills in applying deep learning to complex engineering challenges.
• Capacity for independent learning and critical thinking in the rapidly evolving field of AI in engineering.
• Ability to communicate complex technical concepts related to deep learning in engineering effectively.

• The Deep Learning for Engineering module is covered in lectures and reinforced by practical programming exercises and case studies.
• Two-hour sessions delivered in a single term.
• Students are encouraged to make use of staff 'Surgeries' (otherwise "Office Hours") to discuss any aspect of the module with teaching staff on a one-to-one basis. These are sign-up sessions available for up to one hour per week.
• Coursework (code and report) is appropriate as a mode of assessment for this module because it allows students to work on realistic engineering problems.

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