Παρουσίαση/Προβολή

Course Syllabus

1. Introduction: Introduction and background to the topic
2. Fundamentals:
   1. Machine Learning: concept, categories, applicability, features and extraction
   2. Data: data structures, use, storage (FAIR principles), platforms and databases
   3. Features: importance and extraction, examples, workflows
   4. Explainability: use cases and critical assessment
   5. Ensemble learning: boosting, trapping
3. Methods and algorithms:
   1. Recap: Unsupervised and supervised learning: selected methods (principal component analysis, kmeans, DBSCAN; regression)
   2. Neural Networks: mathematical build-up, hyperparameters, selected networks (long short-term memory, graph neural networks), autoencoders, physics-informed neural networks
   3. Generative models: concepts and differences from non-generalized models, examples (variational autoencoders, general adversial networks)
   4. Hybrid models: concepts, examples
   5. Large language models: transformers, tokenization
4. Design of soft materials & (bio)molecules:
   1. Structure: proteins (AlphaFold), molecules, fingerprinting/featurization, selected prediction models
   2. Properties: embeddings, use cases
5. Machine Learning and computer simulations:
   1. Potentials: development generations, distinct energy descriptions
   2. : sInteractions: Short-, medium-, and long-range

Course Objectives/Goals

Upon successful completion of the course students will be able to:

• Gain experience with data and databases and be aware of the fairness in terms of generating, using, and archiving data.
• Become familiar with advanced concepts of Machine Learning with emphasis in Natural Sciences and Engineering.
• Be able to critically assess the applicability, explainability, and generalizability of Machine Learning methods.
• Understand the conceptual differences, strengths & weaknesses of standard with respect to generative learning models.
• Be able to critically compare Machine Learning schemes and algorithms.
• Be able to express a Machine Learning algorithm in form of a pseudocode.

• Express solid-state structures and molecules as embeddings and be able to provide examples and ideas of molecular fingerprints.

•  Understand the concept of Machine Learning potentials and explain their applicability.

• Gain a practical hands-on experience with advanced Machine Learning algorithms

Bibliography

• Goodfellow, Y. Bengio, A. Courville, Deep Learning (Adaptive Computation and Machine Learning series), The MIT Press, USA (2016)
• P. Deisenroth, A. A. Faisal, C.S. Ong, Mathematics for Machine Learning, Cambridge University Press (2020) 
• P. Murphy, Machine Learning a probabilistic perspective, The MIT Press (2012) 
• Shalev-Shwartz, S. Ben-David, Understanding Machine Learning, Cambridge University Press (2014)
• M. Erdmann, J. Glombitza, G. Kasieczka, U. Klemradt, Deep Learning for Physics Research, World Scientific (2021).

Instructional Methods

Distance learning through video conference

Use of slides

• Use of algorithms
• Use of an asynchronous e-learning platform (e-class):
  • Bibliography of the course
  • Slides of the course*
  • pseudo/algorithms*
  • examples of applications*

• Communication through the e-class platform, use of the discussion area facility with topics, email as well as fixed office hours announced
• Students' assignments are received and corrected via the platform (e-class)

* Creative Commons CC-BY-ND-3.0 licenses

Assessment Methods

The language of the course and evaluation in english.

Final assessment:

The final grade is the sum of the following grades:

- 30% of the theoretical/computational assignment

- 30% of the assignment presentation

- 40% of the grade of the final written examination