Analysis

Learner workload

343h 0min

M1-INTRO: Introduction to Artificial Intelligence and Machine Learning
11h 0min

M2-DATA-ANALYSIS: The Data Analysis Process
11h 0min

M3-SIMPLE-ML: Introduction to Simple Machine Learning Methods
11h 0min

M4-CLUST: Cluster Analysis
11h 0min

M5-CONVEX-OPTI: Convex Optimization
11h 0min

M6-OPTI-LEARN: Optimization-based Machine Learning
11h 0min

M7-EVAL: Evaluating Model Performance
11h 0min

M8-INTERPRET-TABULAR: Interpretability of Models on Tabular Data
11h 0min

M9-AUTODIFF-ANN: Introduction to Neural Networks and Automatic Differentiation
11h 0min

M10-DEEP-LEARN: Deep Learning
11h 0min

M11-DEEP-LEARN-ADVANCED: Advanced Approaches in Deep Learning
11h 0min

M12-INTERPRET-DEEP: Interpretability Methods for Deep Learning
11h 0min

M13-DEEP-LEARN-SEQ: Deep Learning for Sequential Data
11h 0min

M14-ENSEMBLE: Ensemble Methods
11h 0min

M15-DIMRED: Dimensionality Reduction
11h 0min

M16-EMBED: Embeddings
11h 0min

M17-GP-HYPEROPT: Gaussian Processes and Hyperparameter Optimization
11h 0min

M18-RL: Reinforcement Learning
11h 0min

M19-DEEP-RL: Deep Reinforcement Learning
11h 0min

M20-SVM: Support Vector Machines
11h 0min

M21-SEARCH: Search Methods
11h 0min

M22-ADVERSARIAL-SEARCH: Search Methods in Adversarial Contexts
11h 0min

M23-METAHEURISTICS: Metaheuristic Optimization
11h 0min

M24-STATE-SPACE: State-space Approaches in Control
11h 0min

M25-BAYES-NET: Bayesian Networks
11h 0min

M26-GAMING: AI and Gaming
11h 0min

M27-FAIRNESS: Fairness in Machine Learning
11h 0min

Team Project
46h 0min

Machine Learning School