DT8807 Avanserte emner innen dyplæring med python

Upon Completion of This Course, You'll Have:

1. An understanding of the capabilities and limitations of machine learning (ML), and the knowledge of how to formulate your problem to solve it effectively.
2. An understanding of convolution neural nets, recurrent neural nets, and state-of-the-art transfer learning models.
3. An effective process for developing your machine learning pipeline to tackle real world problems such as machine vision, text understanding and time series prediction.
4. The skills required to deploying, monitoring, and evaluating the ML model, as well as assessing its relevance, and the uses of different ML models.
5. The basis required to collect, process, and utilize data efficiently.
6. The basic skills required to select the right platform to deploy your model (cloud, edge device, hybrid) and how to configure it to achieve the required performance.
7. The ability to document and communicate the results of your ML approach and guide your coding and ML efforts in the right direction.

• You are expected to know some basic linear algebra and basic programming skills in Python.
• You are expected to be motivated and like working in teams.

An updated reading list will be provided before the course. To name but a few:

• Josh Patterson and Adam Gibson (2017). Deep learning: a practitioner’s approach. O'Reilly.
• Andreas C. Muller and Sarah Guido (2017). Introduction to machine learning with python. O'Reilly.
• Mohamed Elgendy (2020). Deep learning for vision systems. O'Reilly.

• Teaching approach: 5 hours each (lectures - practice - project work).