Vorlesungen

Automated theorem proving is a subfield of mathematical logic that concerns itself with proving mathematical theorems fully automatically using computer programs. These programs are called automated theorem provers. They can be used as stand-alone programs to solve logic problems or in tandem with interactive theorem provers (also called proof assistants) to discharge proof obligations that arise in interactive proofs.

In this course, we will review some of the main approaches to automated theorem proving. The course focuses on the theory of theorem proving. Stylistically, the course has a mathematical flavor (with definitions, lemmas, proofs, etc.).

The course is based on the materials from Uwe Waldmann’s courses Automated Reasoning I and Automated Reasoning II at Saarland University. We are grateful to him for letting us use his materials.

Please enroll yourself for the course using this key: ATP2425

Enrolment key:

cMPy&m-!7r3zsk#?yZvQ

Course website: https://www.dbs.ifi.lmu.de/cms/studium_lehre/lehre_master/dma2425/index.html

Moodle Enrollment key: ZEa!g6e+A5Upm&kYQFh0

Content

The vast increase in data volume in almost every field results in increased difficulty or even impossibility for information analysis. Especially in areas such as biological measurement evaluation (e.g. gene sequencing, micro-array processes …) or data transaction in large telecommunications or network operators, using data without computational aid is inconceivable. The research area “Knowledge Discovery in Databases (KDD)” investigates solutions to these problems. It combines statistics, machine learning, database systems, and (semi-) automatic extraction methods for valid, new, and potentially useful knowledge from large databases. The term data mining in this context refers to the fundamental step in the KDD process, in which the actual analysis of the data is carried out. Data mining is often applied to large amounts of operational data that are managed separately in so-called data warehouses. The frequently used term Business Intelligence describes, among other things, the application of data mining algorithms to the information provided by a data warehouse in order to support targeted decision-making processes. The lecture gives an overview of the basics of the most important KDD techniques. Particularly: Classification, regression/trend detection, clustering, outlier detection, association rules, and process mining.

To deepen the lecture, exercises are offered in which the presented procedures are further explained and illustrated with practical examples.

Organization

• Tanveer Hannan (he/him)
• Mamdouh Aljoud (he/him)

Planning

Computational Intelligence is the study of algorithms that exhibit (seemingly) intelligent behavior. Historically, it encompasses several fields of computer science, including logic, optimization, and multi-agent system. Our goal is to give the recently rapid developments in artificial intelligence a broader context by connecting them to their historical and scientific foundations. Thus, in this course we discuss intelligent system specified in an increasingly complex manner as well as relevant examples and literature.

Key to register: coin24

Welcome to the course webpage for Parallel and High Performance Computing for winter-term 2024/25 at LMU Munich. Here you will find details on the lecture and the accompanying practical lab exercises.

Lecture slides will be made available chapter-by-chapter through this webpage.

Material from the lab exercises will be available via Moodle.

Here is the enrollment key: ParallelPioneers

Content

Parallel computing is concerned with using multiple compute units to solve a problem faster or with higher accuracy. Historically, the main application area for parallel machines is found in engineering and scientific computing, where high performance computing (HPC) systems today employ tens- or even hundreds of thousand compute cores.

The application area for parallel computing has, however, expanded recently to essentially include all areas of information technology. Virtually all servers, desktop, and notebook systems, and even smartphones and tablets are today equipped with CPUs that contain multiple compute cores. In each case, the potential for these systems can only be fully realized by explicit parallel programming. As such, understanding the benefits, challenges, and limits of parallel computing is increasingly becoming a "must have" qualification for IT professionals.

This course addresses the increasing importance of parallel and high performance computing and is covering three interwoven areas: Parallel hardware architectures, parallel algorithm design, and parallel programming. The successful student will be able to identify potentials for parallel computing in various application areas, judge the suitability of contemporary hardware architectures for a parallel computing problem and understand efficient implementation strategies using modern parallel programming approaches.

Audience

Lab Exercises