Intendierte Lernergebnisse
Welcome to an exciting journey into Data Mining, Synthetic Data, and Knowledge Discovery. This comprehensive course will provide you with a deep understanding of the fundamental approaches and techniques employed in these domains. Our primary objective is to equip you with the essential knowledge and skills necessary to tackle a wide range of supervised, unsupervised, and synthetic data generation challenges across various industries and research domains. You will master the art of extracting valuable insights, patterns, and knowledge from large and complex datasets, empowering you to make informed decisions and accurate predictions. In addition, you will deeply explore the realm of synthetic data generation, particularly harnessing the power of architectures like Autoencoders, Generative Adversarial Networks (GANs) and diffusion models. Throughout the course, you will have the unique opportunity to explore real-world use cases and delve into the latest applications from cutting-edge research. By immersing yourself in practical scenarios, you will gain a firsthand understanding of how these techniques are applied, enhancing your appreciation for their significance in today's data-driven world.
Lehrmethodik inkl. Einsatz von eLearning-Tools
The teaching methodology for this course on Data Mining, Synthetic Data, and Knowledge Discovery integrates several instructional strategies to ensure comprehensive understanding and real-world application of concepts. Regular online tutorials will provide students with practical examples and exercises to reinforce lecture content, clarify doubts, and encourage questions. Students will undertake practical projects, applying acquired knowledge and skills to real-world problems, working with complex datasets, extracting insights, patterns, and knowledge, and generating synthetic data using architectures like Autoencoders, GANs, and diffusion models. The eLearning platform will host a variety of online resources, including research papers, articles, and tutorials, offering additional insights into the latest field advancements. In-class quizzes and assessments will evaluate students' understanding and practical application of concepts, with feedback provided for improvement.
Inhalt/e
Data Mining and Knowledge DiscoveryData preprocessingSupervised Learning (Support Vector Machine (SVMs), Bayes Classifiers, Decision Trees)Unsupervised Learning and Clustering (K-means, hierarchical clustering)Dimensionality Reduction (Singular Value Decomposition (SVD), Principal Component Analysis (PCA) Regularization TechniquesKernel ModelsEvaluation MetricsSynthetic DataIntroduction to Neurocomputing (Activation Functions, Backpropagation)AutoencodersVariational Autoencoders (VAEs)Generative Adversarial Networks (GANs)Diffusion models
Erwartete Vorkenntnisse
This course's prerequisites typically include basic programming skills, preferably in Python, as many tasks will involve coding. A solid mathematical background in linear algebra, calculus, and statistics is crucial for understanding and implementing data mining techniques.
Curriculare Anmeldevoraussetzungen
No requirements.
Literatur
Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, and Jian Pei: This book provides a comprehensive introduction to the concepts and techniques of data mining.Pattern Recognition and Machine Learning by Christopher Bishop: Although not exclusively focused on data mining, this book provides a comprehensive introduction to pattern recognition and machine learning, which are essential concepts for data mining and knowledge discovery.Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster: This book provides a comprehensive introduction to generative modeling using deep learning architectures like Autoencoders, GANs, and diffusion models.