In an era where digital technologies are reshaping industries and daily life, the environmental impact of AI systems has become a growing concern. This course explores efficient AI methodologies to address these challenges. From deep learning model compression to low-bit quantization and collaborative inference, we delve into techniques that enhance computational efficiency and reduce energy consumption. We will also focus on low-bit quantization specifically for large language models (LLMs), showcasing cutting-edge open-source tools and models. Join us to learn how to build sustainable AI systems while pushing the boundaries of innovation.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

Practical Computer Vision in PyTorch is a comprehensive, hands-on course for developers and practitioners eager to explore computer vision with PyTorch. It spans image classification, object detection, segmentation, and generative modeling. Emphasizing implementation, participants work through coding demos and projects with industry-standard tools and libraries. By the end, they will be able to build and fine-tune computer-vision models for real-world applications.

Digital systems offer a great opportunity to significantly reduce carbon emissions and can contribute to the efficient use of energy. However, all systems also need energy. This area of tension is addressed in the course: Sustainability in the digital age - Energy-Efficient Software Development. To effectively reduce the carbon footprint of digitalization, it is necessary to apply algorithmic efficiency and sustainability by design as guiding principles in digital engineering. We will introduce strategies to develop software that prioritizes minimized energy consumption through optimal coding and green testing practices. We will look at how the CO2 emissions of operating software applications can be measured and calculated. How to measure the performance and energy consumption of Large Language Models will be covered as well. Further we share approaches how to use advances in hardware technology and operate digital systems efficiently in data centers based on eco-friendly and cost-effective capacity management strategies.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

Mit diesem Kurs taucht Dozentin Prof. Dr. Bettina Just von der Technischen Hochschule Mittelhessen ein vorerst letztes Mal mit Ihnen in die Grundlagen des Quantencomputings ein. Sie lernen zuerst den Mechanismus den Phase-Kickbacks kennen, der die Basis vieler Quantenalgorithmen ist.

Hier werden Sie auch an einigen bereits bekannten Beispielen von Quantenalgorithmen vorbeikommen. Daran anschließend wird es um Quantenfehlerbehebung gehen. Weiter wird erläutert, warum Quantenbits komplexe (und nicht einfach reelle) Amplituden haben. Der Kurs schließt mit einem Ausflug ins adiabatische Quantencomputing, das ganz anders funktioniert als Quantencomputing mit dem Schaltkreismodell und ein vielversprechender Ansatz für die Lösung von Optimierungsproblemen ist.

Bitte beachten Sie: Anders als die meisten anderen Kurse auf openHPI steht dieser Kurs nicht unter einer Creative-Commons-Lizenz. Vervielfältigung und Veränderung der Materialien, außer im Zusammenhang mit Ihrer Teilnahme an diesem Kurs, sind nicht gestattet.

Efficiency in computer science often refers to the runtime or memory usage, that a certain algorithm needs to produce an output. However, efficiency can also describe the amount of energy, that is consumed by the hardware during the runtime of an algorithm.

This course explains the relevant computer architecture components, as well as different coding techniques, that allow computer scientists to design and program energy efficient algorithms. Participants will learn how intrinsic functions work and how they can be applied to an algorithm to reduce its energy consumption. In addition, the course presents a hands-on approach to measuring energy consumption of programs using the Intel VTune Profiler tool. In contrast to the existing clean-IT courses on openHPI, this course provides a deeper dive into specific, energy efficient, architecture coding techniques.

Bayesian data analysis is increasingly becoming the tool of choice for many data-analysis problems.

This free course on Bayesian data analysis will teach you basic ideas about random variables and probability distributions, Bayes' rule, and its application in simple data analysis problems. You will learn to use the R package brms (which is a front-end for the probabilistic programming language Stan). The focus will be on regression modeling, culminating in a brief introduction to hierarchical models (otherwise known as mixed or multilevel models).

This course is appropriate for anyone familiar with the programming language R and for anyone who has done some frequentist data analysis (e.g., linear modeling and/or linear mixed modeling) in the past.

Whether we stream our favorite series, develop new drugs or have us being chauffeured by a self-driving car -- machine learning is an essential part of our modern life, and of our future. But the growing amount of data and our increasing demands pose difficulties for today's classical computers. Can quantum computing overcome these challenges? What potentials does the emerging field of quantum machine learning have?

In this course, we will not only learn about quantum machine learning and its prospects, but we will also solve concrete tasks with both classical and quantum models. This course is aimed at students, experts and enthusiasts of quantum computing or machine learning. Prior knowledge about quantum computing or quantum information are strongly recommended.

In dieser Fortsetzung des ersten Quantencomputing-Kurses auf openHPI Einführung in das Quantencomputing (hier Teil 1) erweitert Dozentin Prof. Bettina Just von der Technischen Hochschule Mittelhessen die Grundlagen des Quantencomputings. Sie lernen, wie der Algorithmus zur Teleportation funktioniert, und wie klassische logische Gatter auf Quantenschaltkreisen simuliert werden können. Danach gibt es Mathematik, immer mit Beispielen aus dem Quantencomputing und nur genauso viel, wie es für das Verständnis der Folgekurse erforderlich ist: Vektoren, Matrizen, Tensoren, komplexe Zahlen, und eine Detaillierung der Idee, warum Quantenalgorithmen so schnell sind.

Bitte beachten Sie: Anders als die meisten anderen Kurse auf openHPI steht dieser Kurs nicht unter einer Creative-Commons-Lizenz. Vervielfältigung und Veränderung der Materialien, außer im Zusammenhang mit Ihrer Teilnahme an diesem Kurs, sind nicht gestattet.

Compared to Cloud Computing, which is centralized in computing and data storage, Edge Computing brings computation and data storage closer to data sources.

Edge AI combines edge computing and AI technology and has become a rapidly developing field in the past few years. Edge AI enables AI computing directly on the edge or client device, enhancing power efficiency, supporting low latency, and solving data privacy problems.

Therefore, what improvements need to be made to traditional deep learning algorithms in Edge AI scenarios? This course teaches you about deep model compression and optimization techniques, decentralized and collaborative deep learning approaches and algorithms, software, and hardware for Edge AI.

In this course you will learn how to use Qiskit for working with quantum computers. Qiskit is an SDK for working at the level of pulses, circuits, algorithms and application modules. During the first week you will explore the available tools to run your experiments on IBM Quantum computers in the cloud, write your first lines of Qiskit code, do a recap of the fundamentals of quantum computing and understand how to run experiments both on simulators and on quantum devices. During the second week you will use everything you have learnt to implement two of the first quantum computing algorithms.

Du hast bereits den Java-Kurs auf openHPI gemacht und die Einführung in Collections war dir zu schnell oder nicht detailliert genug? Du willst Datenstrukturen in Java besser kennenlernen? Du wolltest immer schon mal wissen, was eigentlich Iteratoren sind und welche Vorteile diese gegenüber von Schleifen haben? Welche Datenstrukturen für welche Anwendungsfälle schneller oder besser geeignet sind?

Auch Duke und seinen neuen Freund Big O beschäftigen diese Fragen in ihrem aktuellen Fall und sie brauchen wieder mal Deine Hilfe! Begleite Duke und seine Freunde auf ihrer spannenden Reise durch die wunderbare Welt der Algorithmen und Datenstrukturen in Java. Spoiler: mehr Drama, mehr Action und höchstens ein Papagei.

Digitalization is a game changer in the pursuit of a sustainable future. The latest digital technologies and applications like cloud, AI, and mobile devices enable us to achieve the Sustainable Development Goals and reduce carbon emissions in many sectors. Yet computer systems themselves have an immense energy requirement for their countless devices, data centers, applications and global networks. To effectively reduce the carbon footprint of digitalization, it is necessary to apply algorithmic efficiency and sustainability by design as guiding principles in digital engineering. The clean-IT Forum is the international platform to exchange ideas, recent research findings and applications to make digital technologies more energy-efficient.