DAT255 Deep Learning Engineering

Contents and structure

Deep learning has become a key driver of advances in artificial intelligence, powering breakthroughs from self-driving cars to large language models and many of the AI applications we interact with daily. This course explores deep learning's core applications in computer vision, natural language processing, and recommendation systems, providing you with the foundation to apply these techniques across diverse domains.

Beyond theoretical foundations, you'll gain hands-on experience in the complete lifecycle of deep learning projects: from design and implementation to deployment and maintenance. Using industry-standard frameworks and best practices, you'll learn to build production-ready deep learning solutions that scale. The course bridges the gap between traditional software engineering principles and the unique challenges of deep learning systems, preparing you for real-world AI systems development and deployment.

Learning Outcome

Knowledge

• Able to explain fundamental concepts, architectures, and training algorithms in deep learning, including optimization, regularization, and neural network design principles.
• Able to explain how deep learning can be used to solve practical problems from a variety of domains.
• Able to explain the limitations and challenges of using deep learning for real-world tasks.
• Understanding of the difference between deep learning as a discipline in itself, and deep learning engineering. This includes how deep learning relates to software engineering more broadly and an understanding of machine learning engineering and MLOps, including model versioning, monitoring, and maintenance.
• Understanding of deep learning model evaluation metrics and validation strategies specific to different application domains.
• Understanding of the main drivers of progress in artificial intelligence.

Skills

• Able to solve concrete, practical problems from computer vision, natural language processing, and recommender systems using deep learning.
• Has hands-on experience with model deployment pipelines, including both local deployment and cloud-based solutions, with consideration for scalability and performance.
• Can effectively integrate and utilize large language models and other generative AI tools within software applications.
• Can develop and use modern, state-of-the-art software tools and frameworks for data analysis, machine learning, and visualization.

General competence

• Able to formulate and complete deep learning-based projects.
• Able to evaluate and select appropriate deep learning approaches for given problems, considering both technical and practical constraints.
• Able to present deep learning projects through oral presentations and technical documentation, including well-structured code, experimental results, and performance analyses.

Entry requirements

None.

Recommended previous knowledge

The student should have programming skills at the bachelor level in a computer science or computer engineering program. Experience with Python will be a significant advantage. Exposure to core parts of software engineering will be advantageous. Several online resources for acquiring the recommended background knowledge will be provided at the beginning of the course.

You should have completed at least one course in machine learning at the level of DAT158 (HVL) or INF264 (UiB).

Teaching methods

DAT255 is partly a "flipped classroom" course with a significant online, on-demand component. The classroom lectures will expand on the topics covered by the online course material and explore other related material. There will also be hands-on labs where you will work on topics covered by the lectures and your course project, assisted by the lecturer and fellow students.

In Førde, the course will be given as a fully digital offering.

Compulsory learning activities

The students will complete a deep learning project on a chosen topic during the course.

Assessment

The exam has two parts:

1. A group project report that counts for 50% of the final grade
2. Written school exam, 2 hours, that counts for 50% of the final grade.

Both parts of the exam must result in a passing grade to get a final grade in the course. If a student fails one of the parts, the part can be retaken separately.

Grading scale is A-F where F is fail.

Examination support material

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