Probability and Random Processes
Explore random variables, probability distributions, and stochastic processes through intuitive explanations and practical applications in parameter estimation and noisy measurements.
Engineering Econometrics
This course will be exclusively quantitative and an application to business/management-related problems. It is connected with problem sets and real-life cases to know the relevance of a particular problem and the decision-making thereof.
Autocorrelation
Explore autocorrelation in data analytics: its concept, importance, testing methods, and applications. Includes a practical Python demo for hands-on learning and skill development.
Econometrics
Comprehensive econometrics course covering regression analysis, time series, heteroscedasticity, autocorrelation, and advanced estimation techniques. Includes practical exercises and exam preparation materials.
E2EML - How Selected Models and Methods Work
Explore key machine learning algorithms and methods, including decision trees, Bayes Theorem, autocorrelation, Support Vector Machines, and k-nearest neighbors, in this comprehensive video series.
Communication System Fundamentals
Explore key concepts in communication systems, including data transmission, noise, signal processing, and network protocols, to enhance understanding of modern telecommunications.
Learning and Exploiting Statistical Dependencies in Networks - 2007
Explore statistical dependencies in networks, focusing on learning joint distributions, network navigation, and structure indexing. Discover applications in web mining, bioinformatics, and fraud detection.
Cross-Validation Revisited: Evaluating Machine Learning Models with Non-IID Data
Explore challenges and techniques for evaluating machine learning models with non-IID data, focusing on spatial autocorrelation, concept drift, and phylogenetic non-independence.
Identifying Functional Brain Networks with Conditional Independence Graphs
Explore advanced techniques for constructing functional brain networks using conditional independence graphs and partial correlation, addressing limitations of traditional correlation methods.
A Few Quantitative Thoughts on Parking in Marburg - RC3 - 2020
Analyse des données de stationnement à Marburg : tendances, modèles et prévisions pour optimiser l'utilisation des espaces de stationnement et améliorer la planification urbaine.