02456 Deep Learning

Rapporteur: Jes Frellsen

Type of assessment: Oral examination and reports (hand in report first and the poster presentation to allow for questions based on reports)

Aids: All

Evaluation: 7 step scale, internal examiner

Learning objectives

  • Demonstrate knowledge of machine learning terminology such as likelihood function, maximum likelihood, Bayesian inference, feed-forward, convolutional and Transformer neural networks, and error back propagation.
  • Understand and explain the choices and limitations of a model for a given setting
  • Apply and analyze results from deep learning models in exercises and own project work
  • Plan, delimit and carry out an applied or methods-oriented project in collaboration with fellow students and project supervisor*
  • Assess and summarize the project results in relation to aims, methods and available data*
  • Carry out the project and interpret results by use of computational framework for GPU programming such as PyTorch*
  • Structure and write a final short technical report including problem formulation, description of methods, experiments, evaluation and conclusion*
  • Organize and present project results at the final project presentation and in report*
  • Read, evaluate and give feedback to work of other students

* If AI is used in this phase, then it needs to be documented and critically assessed. A checklist will be provided and should be handed in as part of the report.

Content

Course outline week 1-8:

  1. Introduction to statistical machine learning, feed-forward neural networks (FFNN) and error backpropagation. Part I do it yourself on pen and paper.
  2. Introduction to statistical machine learning, feed-forward neural networks (FFNN) and error backpropagation. Part II do it yourself in NumPy.
  3. Introduction to statistical machine learning, feed-forward neural networks (FFNN) and error backpropagation. Part III PyTorch.
  4. Convolutional neural networks (CNN) + presentation of student projects.
  5. Sequence modelling for text data with Transformers.
  6. LLMs and chatbots (i.e. how they work)
  7. Tricks of the trade and data science with PyTorch, prompting of chatbots and the use for copilot for writing reports and code (including prompting guidelines) + Start of student projects.

Starting from week 6 and full time from week 9 and the rest of the term will be spent on tutored project work