Machine Learning

Machine learning investigates and develops methods that allow computers to infer or recognize patterns using datasets of various sizes, whether for exploratory purposes or to accomplish specific tasks. It has applications in numerous areas, from information systems and bioinformatics to computer vision, robotics, and security, among others.

Groups and Researchers in this Field


Perceiving Systems

Michael J. Black is one of the founding directors of the Max Planck Institute for Intelligent Systems, where he leads the Perceiving Systems Department. His research addresses a variety of topics relating to computer vision and perception: the statistics of natural scenes and their motion; articulated human motion pose estimation and tracking; the estimation of human body shape from images and video; the representation and detection of motion discontinuities; and the estimation of optical flow. His early work on optical flow has been widely used in Hollywood films. He also does research on neural engineering for brain-machine interfaces and neural prostheses. He is an honorary professor at the University of Tübingen, visiting professor at ETH Zürich, and adjunct professor (research) at Brown University. Read more

Michael J. Black

Michael J. Black

MPI-IS, Scientific Director

Probabilistic Numerics

Philipp Hennig holds the Chair for the Methods of Machine Learning, and is an adjunct scientist at the Max Planck Institute for Intelligent Systems. He studied Physics in Heidelberg, Germany and at Imperial College, London, before moving to the University of Cambridge, UK, where he attained a PhD in the group of Sir David JC MacKay with research on machine learning. Since this time, he is interested in connections between computation and inference. With international collaborators, he helped establish the field of probabilistic numerics. His research was supported, among others, by the Emmy Noether Programme of the German Research Union (DFG), an independent Research Group of the Max Planck Society, and a Starting Grant of the European Commission. Read more

Philipp Hennig

Philipp Hennig

MPI-IS, Adjunct Faculty

Machine Learning and Large-scale Data Mining Methods

Manuel Gomez Rodriguez is a research group leader at the Max Planck Institute for Software Systems. He is interested in developing machine learning and large-scale data mining methods for analysis and modeling of large real-world networks and processes that take place over them. His research comprises several dimensions: developing models of these networks and processes, assessing their theoretical properties and limitations; developing machine learning algorithms to fit the models and computational methods to influence processes over networks; and validating models and methods on gigabite- and terabyte-scale real-world datasets. Ultimately, he aims to provide computational tools with applications in a variety of domains, e.g. social and information sciences, economics, decision theory, causality, and epidemiology. Read more

Manuel Gomez Rodriguez

Manuel Gomez Rodriguez

MPI-SWS, Faculty

Rationality Enhancement

Falk Lieder leads the Rationality Enhancement Group at the MPI for Intelligent Systems in Tübingen. Our mission is to lay the scientific and technological foundations for helping people thrive. To achieve this, our research focuses on three key aspects of human effectiveness: cognitive growth, goal setting, and goal achievement. Our approach combines computational modeling, behavioral experiments, artificial intelligence, and machine learning with developing practical tools and interventions. To promote cognitive growth, we investigate the mechanisms of metacognitive learning and translate the resulting insights into cognitive tutors that teach people optimal decision strategies, brain training programs for improving people’s executive functions, and reflective learning apps for self-improvement. Our research on goal-setting investigates what makes goals effective, builds computational models of optimal goal-setting, and develops intelligent apps that help people (learn to) set better goals. Our research on goal-achievement develops intelligent personal assistants and productivity apps that help people achieve their goals more effectively. Read more

Falk Lieder

Falk Lieder

MPI-IS, Research Group Leader

Real Virtual Humans

Gerard Pons-Moll is the head of the Emmy Noether independent research group "Real Virtual Humans" and senior researcher at the Max Planck for Informatics (MPII) in Saarbrücken. His research lies at the intersection of computer vision, machine learning and computer graphics -- with special focus on analyzing people in videos, and creating virtual human models by "looking" at real ones. His research has produced some of the most advanced statistical human body models of pose, shape, soft-tissue and clothing, as well as algorithms to track and reconstruct 3D people models from images, video, depth, and IMUs. His work has received several awards including the prestigious Emmy Noether Grant (2018), a Google Faculty Research Award (2019), a Facebook Reality Labs Faculty Award (2018), and recently the German Pattern Recognition Award (2019), and several best paper awards. Read more

Gerard Pons-Moll

Gerard Pons-Moll

MPI-INF, Senior Researcher

Computer Vision

Bernt Schiele is the founder of the Computer Vision and Multimodal Computing Department at the Max Planck Institute for Informatics, and head of its Computer Vision research area. His group focuses on multimodal sensor processing as well as computer vision. In computer vision, they consider problems of 3D understanding of images and video, such as object class recognition, people detection and tracking, and understanding traffic scenes. In multimodal computing, they are focusing on human activity recognition as a means to study how ubiquitous or wearable computing may benefit from better sensor understanding. Their research also involves machine learning, which is instrumental to inferring higher-level information from noisy sensor data and handling large-scale multimodal databases and sensor streams. Read more

Bernt Schiele

Bernt Schiele

MPI-INF, Scientific Director

Empirical Inference

Bernhard Schölkopf directs the Empirical Inference Department at the Max Planck Institute for Intelligent Systems. The department investigates problems of empirical inference, i.e. inference based on empirical data. The type of inference can vary, including for instance inductive learning (estimation of models such as functional dependencies that generalize to novel data sampled from the same underlying distribution), or the inference of causal structures from statistical data (leading to models that provide insight into the underlying mechanisms, and make predictions about the effect of interventions). Empirical data may also vary, from sparse experimental measurements (e.g. microarray data) to visual patterns. The department uses theoretical, algorithmic, and experimental approaches to study these problems. Read more

Bernhard Schölkopf

Bernhard Schölkopf

MPI-IS, Scientific Director

 

Machine Teaching

Adish Singla is a faculty member at the Max Planck Institute for Software Systems. He is interested in the design of AI-ML methods that interact with, learn from, and teach other learning entities such as humans, robots, and machines. His research interests span various application domains, including the design of intelligent tutoring systems for personalized education, social robotics, and adversarial machine learning. The theoretical aspects of his work include machine learning (esp. online, active), AI (esp. probabilistic modeling), and optimization (esp. submodular). The focus is towards designing principled techniques that are both theoretically well-founded with strong provable guarantees and are practically applicable. Read more

 

Adish Singla

Adish Singla

MPI-SWS, Faculty

Exploratory Data Analysis

Jilles Vreeken is a senior researcher in the Databases and Information Systems Department at the Max Planck Institute for Informatics, and leads the Exploratory Data Analysis independent research group at the Cluster of Excellence on Multimodal Computing and Interaction. His research focuses on exploratory data mining: developing theory and algorithms to identify interesting structures within given data. Of particular value here are statistical methods, such as information-theoretic principles of minimum description length and maximum entropy. Next, he develops efficient algorithms to extract these structures from large and complex data, and investigates how they can be used in a range of applications, including identifying rare diseases, e-health, bio-informatics, market analysis, product recommendation, etc. Read more

Jilles Vreeken

Jilles Vreeken

MPI-INF, Senior Researcher

Research at Partner Universities