Machine Learning School

M1-INTRO: Introduction to Artificial Intelligence and Machine Learning M2-DATA-ANALYSIS: The Data Analysis Process M3-SIMPLE-ML: Introduction to Simple Machine Learning Methods M4-CLUST: Cluster Analysis M5-CONVEX-OPTI: Convex Optimization M6-OPTI-LEARN: Optimization-based Machine Learning M7-EVAL: Evaluating Model Performance M8-INTERPRET-TABULAR: Interpretability of Models on Tabular Data M9-AUTODIFF-ANN: Introduction to Neural Networks and Automatic Differentiation M10-DEEP-LEARN: Deep Learning M11-DEEP-LEARN-ADVANCED: Advanced Approaches in Deep Learning M12-INTERPRET-DEEP: Interpretability Methods for Deep Learning M13-DEEP-LEARN-SEQ: Deep Learning for Sequential Data M14-ENSEMBLE: Ensemble Methods M15-DIMRED: Dimensionality Reduction M16-EMBED: Embeddings M17-GP-HYPEROPT: Gaussian Processes and Hyperparameter Optimization M18-RL: Reinforcement Learning M19-DEEP-RL: Deep Reinforcement Learning M20-SVM: Support Vector Machines M21-SEARCH: Search Methods M22-ADVERSARIAL-SEARCH: Search Methods in Adversarial Contexts M23-METAHEURISTICS: Metaheuristic Optimization M24-STATE-SPACE: State-space Approaches in Control M25-BAYES-NET: Bayesian Networks M27-FAIRNESS: Fairness in Machine Learning Team Project