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Solving the Powertrain Puzzle : 10th Schaeffler Symposium April 3/4, 2014
Every four years, Schaeffler provides an insight into its latest developments and technologies from the engine, transmission and chassis as well as hybridization and electric mobility sectors. In 2014 the Schaeffler Symposium with the motto “Solving the Powertrain Puzzle” took place from 3th to 4th of April in Baden-Baden. Mobility for tomorrow is the central theme of this proceeding. The authors are discussing the different requirements, which are placed on mobility in different regions of the world. In addition to the company's work in research and development, a comprehensive in-house mobility study also provides a reliable basis for the discussion. The authors are convinced that there will be a paradigm shift in the automotive industry. Issues such as increasing efficiency and advancing electrification of the powertrain, automatic and semi-automatic driving, as well as integration in information networks will define the automotive future.
Representation Learning : Propositionalization and Embeddings
This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
