Advanced Topics in Deep Learning

Learning Results

Upon successful completion of the course, students will:

• Have an understanding of how to choose a model to describe a particular type of data.
• Know how to evaluate a learned model in practice.
• Understand the mathematics necessary for constructing novel machine learning solutions.
• Possess a critical understanding of the different characteristics, capabilities and limitations of different advanced deep learning techniques and select the appropriate ones for different cases of complex problem solving
• Be able to follow advancements in the deep learning field and understand the state of the art in deep learning models

Skills acquired

• Data and information retrieval, analysis and synthesis
• Decision making
• Individual work
• Team work
• New research ideas generation
• Promotion of free, creative and inductive thinking

• Training of deep learning architectures
• Bayesian deep learning
• Graph Neural Networks
• Tensor algebra, tensor-based learning
• Multidimensional deep learning
• Domain adaptation, transfer learning
• Explainability, interpretability, trustworthiness of machine learning techniques
• Designing, building and deploying deep learning models in real-world applications

1. Deep Learning, I. Goodfellow, Y. Bengio and A. Courville, MIT Press, 2016, http://www.deeplearningbook.org.
2. Deep Learning with Python, F. Chollert, 2nd Edition, Manning
3. Neural Networks and Deep Learning: A Textbook, C. Aggarwal, 2018, Springer

Related scientific journals:

• IEEE Transactions on Pattern Analysis and Machine Intelligence
• Journal of Machine Learning Research
• IEEE Transactions on Neural Networks and Learning Systems
• NeurIPS conference proceedings