Computing at Scale in Machine Learning: Distributed computing and algorithmic approaches (Modul: 14038)

Learning Outcomes:
Students will obtain an overview on how to solve large-scale computational problems in data science and machine learning using a) parallel approaches from multi-threaded computation on individual machines to implicit parallelism frameworks on compute clusters and b) algorithms and data structures supporting efficient exact or approximate computation with massive data sets in and out of core. In particular they will learn how to analyze relevant probabilistic data structures and algorithms and select and implement appropriate computational approaches for large-scale problems.

Contents:
The focus will be on the following areas:

• A review of memory-compute co-location and its impact on big data computations.
• Solving Machine Learning (ML) work loads using explicit parallelism, specifically multi-threaded computation on an individual machine.
• Introduction of implicit parallelism programming models as implemented for example in MapReduce, Spark and Ray and their application in ML.
• Probabilistic algorithms such as sketching algorithms (incl. CountMinSketch, HyperLogLog) or Bloom filters.
• Implementing ML methods using index data structures such as suffix or kd-trees.

Recommended Prerequisites:
Introduction to machine learning at Master’s level. Advanced knowledge of programming in Python and the Linux command line. 

Detailed information for participants is available at Kurs: 14038 | Computing at Scale in Machine Learning: Distributed computing and algorithmic approaches | WiSe 24/25 | elearning-btu

Introduction to Bioinformatics (Modul: )

Learning Outcomes:
After successfully completing the module, students will have acquired an overview of the fundamentals of bioinformatics. This includes an introduction to relevant molecular processes, scientific instruments to investigate these processes, and the data generated by them. For central computational problems, students will be able to discuss advantages and disadvantages of statistical and basic algorithmic approaches, respectively adapt them to specific biological questions. Students will be able to analyze specific biological data using appropriate software libraries for Python.

Contents:
The focus will be on the basics of the following areas:

• An introduction to molecular biology including relevant scientific instruments and the Omics-data generated by them.
• Pair-wise and multiple sequence alignments, seed-and-extend approaches, and genome indexes
• Evolutionary models and phylogenetic trees
• Signals in sequences: identification of motifs
• Assembly of genomes and transcriptomes
• Gene expression analysis

Recommended Prerequisites:
Good knowledge of discrete probability, algorithms and data structures at the undergraduate level. Advanced knowledge of programming in Python and the Linux command line. 

Detailed information for participants is available at Kurs: 100070 | Introduction to Bioinformatics | WiSe 24/25 | elearning-btu

Bioinformatics (Modul: 13866)

After successfully completing the module, students will be familiar with state-of-the-art problems and methodological approaches used in medical bioinformatics. They will have the ability to familiarize themselves with current research in medical bioinformatics from original research literature, to participte in a technical discussion within the context of international science and present scientific content in written and oral form.
 

Students will learn about specific state-of-the-art problems and methodological approaches used in medical bioinformatics. The applications will range from diagnostics and monitoring patients with sensor, clinical and omics data, to detect clinically relevant states or understand cellular processes relevant to diagnosis and disease as well mechanisms for treating diseases. Methods will include both algorithmic and machine learning approaches. 

Detailed information for participants is available at Kurs: 100051 Seminar Bioinformatics | WiSe 24/25 | elearning-btu

Research Module in Artificial Intelligence (Modul: 14060)

Appointment by arrangement in Sachsendorf

Oberseminar Medizinische Bioinformatik (Modul: 13600)

Appointment by arrangement in Sachsendorf