Alexander Felfernig is a full professor at the Graz University of Technology (Austria) since March 2009 and received his PhD in Computer Science from the University of Klagenfurt. He directs the Applied Software Engineering (ASE) research group. His research interests include configuration systems, recommender systems, model-based diagnosis, software requirements engineering, different aspects of human decision making, and knowledge acquisition methods. In these areas, he is engaged in national research projects as well as in a couple of European Union projects. Alexander Felfernig has published numerous papers in renowned international conferences and journals (e.g., AI Magazine, Artificial Intelligence, IEEE Transactions on Engineering Management, IEEE Intelligent Systems, Journal of Electronic Commerce) and is a co-author of the book on "Recommender Systems" published by Cambridge University Press.

Avoid the most common mistakes and prepare your manuscript for journal editors.

As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).

By Vincent Granville Ph.D, published in September 2022. The book is available here. For my upcoming course based on this book, see here. This book covers the foundations of machine learning, with modern approaches to solving complex problems. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI).

Tilottama Goswami has received a BE degree with Honors in Computer Science and Engineering from the National Institute of Technology, Durgapur; and an MS degree in Computer Science (High Distinction) from Rivier University, Nashua, New Hampshire, United States. She was awarded a PhD in Computer Science from the University of Hyderabad. Presently, Dr. Goswami is Professor in the Department of Information Technology, Vasavi College of Engineering, Hyderabad, India. She has, overall, 23 years of experience in academia, research, and the IT industry. Her research interests are computer vision, machine learning, and image processing.

The State of AI and Machine Learning Report is an annual exploration of the strategies implemented by companies large and small, across industries and continents as they advance in their AI maturity. The 8th edition of this report highlights the prevailing approaches to data management and security, responsible AI, and the significant role played by external data providers in advancing progress. As companies are advancing in AI maturity, we see an even bigger focus on ethics and data diversity.

The eighth edition of the SIDO Lyon will open its doors on September 14 and 15, 2022 at the Cité Internationale de Lyon: 48h top-chrono to decipher the innovation and to concretize its digitalization projects, whatever the level of maturity of its project! SIDO Lyon, the leading trade show in France for IoT, AI, XR and Robotics solutions for the 4.0 transformation of companies, will take place in a few weeks. To make IoT, AI, XR and robotics technologies accessible, to promote human-machine collaboration and thus better use of these technologies by industrial and service companies, to reinvent business processes with new roles, new ways of collaborating and to drive new business models. The market leaders will be in Lyon: Microsoft, Orange, STMicroelectronics, Avnet Silica, Aquitaine Robotics, the Auvergne Rhône-Alpes Region… but also and for the first time, the Pays de la Loire Region, sponsor of the event, which will present through Proxinnov, more than 80m2 dedicated to IoT, AI and Robotics solutions in the region, including a conveyor around which several collaborative robots (or cobots) will simulate a live production line. In total, there will be more than 40% of new exhibitors to discover.

David Millán Escrivá was eight years old when he wrote his first program on an 8086 PC with BASIC language, which enabled the 2D plotting of BASIC equations. He started with his computer development relationship and created many applications and games. In 2005, he completed his studies in IT from the Universitat Politécnica de Valencia with honors in human-computer interaction supported by Computer Vision with OpenCV (v0.96). He had a final project based on this subject and published it on HCI Spanish Congress. In 2014, he completed his Master's degree in artificial intelligence, computer graphics, and pattern recognition, focusing on pattern recognition and computer vision.

Advertiser disclosure: We may be compensated by vendors who appear on this page through methods such as affiliate links or sponsored partnerships. This may influence how and where their products appear on our site, but vendors cannot pay to influence the content of our reviews. Machine learning (ML) books are a valuable resource for IT professionals looking to expand their ML skills or pursue a career in machine learning. In turn, this expertise helps organizations automate and optimize their processes and make data-driven decisions. Machine learning books can help ML engineers learn a new skill or brush up on old ones.

Linear Algebra and Its Applications, 4th Edition Linear algebra is relatively easy for students during the early stages of the course, when the material is presented in a familiar, concrete setting. But when abstract concepts are introduced, students often hit a brick wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations), are not easily understood, and require time to assimilate. Since they are fundamental to the study of linear algebra, students' understanding of these concepts is vital to their mastery of the subject. David Lay introduces these concepts early in a familiar, concrete Rn setting, develops them gradually, and returns to them again and again throughout the text so that when discussed in the abstract, these concepts are more accessible.