Artificial Intelligence and Machine Learning algorithms are involved in a vast variety of scientific and industrial projects, contributing to the solution of the emerging problems, from data modeling and analysis to face recognition and self-driving cars. In recent years, researchers and professionals have been engaging with artificial intelligence algorithms because they have been successfully deployed in an unusually wide range of Scientific, Technological and Business fields. Regardless of the database, their basic function is accomplished by a mathematical model describing the interactions of the variables. The relationship between independent and dependent variables is often non-linear and complex, and mathematical models are aiming at forming a generalized relationship. Their success depends on the mathematical formulation and the underlying theory, the algorithmic application, the prediction errors, as well as the generalization of the results.
- Introduction to Artificial Intelligence
- Mathematical Models (Inverse Problems, Basic Principles of Function Approximation, Approximation Methods, Radial Basis Functions)
- Predictions with Statistics and Machine Learning
- Mass analysis of text & research bibliography. Computational analysis of subjectivity and meaning in a text.
- Regression analysis (Linear & nonlinear regression, Logistic regression, Stepwise simulations and promotion of variables from the beginning and end).
- Artificial Neural Networks (Supervised Learning, Generalized Simulations, Representation & Introduction to Learning Algorithms).
- Error Control (Validation and Test Sets, Overfitting and Overtraining).
- Sensitivity analysis, model development and interpretation of results (isolation of each independent variable in the median & 25-75% percentiles)
- Alternative regression methods (Ensembled Models, Random Forests, Gradient Boost, k-Nearest Neighbors, etc.).
- Optimization algorithms (introduction to heuristics, genetic algorithms, evolutionary, global search, etc.)
- Grouping/Clustering algorithms
Who Should Attend
- professionals who work on data analytics, predictions, and relevant topics
- researchers who want to analyze scientific databases with contemporary machine learning techniques
- anyone interested to understand the fundamental assumptions and hands-on implementation of machine learning algorithms
- introduction to the theoretical background of machine learning
- learn from examples regarding industrial and academic test cases
- transition from explicit statistics to state of the art, non-linear, generalized models
- the course will be held online, via an electronic platform
- the final schedule will be announced soon
- fill in the form below today and gain 50%, early-bird discount. No commitment, you can cancel anytime.