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 M26-GAMING: AI and Gaming M27-FAIRNESS: Fairness in Machine Learning Team Project

M7-EVAL: Evaluating Model Performance

11h 0min
Performance measures for classification and regression; verifying the ability to generalize; bias-variance trade-off; regularization methods
Main Content
Acquisition
1 Lecture content
Content: – Evaluating model performance; – Verifying the ability to generalize: – Split validation; – Stratification; – The validation set and model selection; – Cross-validation; – Performance measures: – For classification: – Why accuracy is not enough; – ROC analysis etc.; – Micro/macro averaging for multi-class problems; – For regression; – Bias vs. variance trade-off; – Regularization methods;
2h 0min
Practice
2 Colab Notebooks
A set of colab notebooks, regarding especially these topics: – Examples on model evaluation; – Performance measures for classification: – Examples with class imbalance – accuracy is not enough; – Performance measures for regression; – Diagnosing underfitting and overfitting; – Regularization methods in classical (shallow) machine learning
3h 0min
0
Investigation
3 Independent study time + review
The estimated additional time required for studying the material independently, using the lecture videos/slides and also referencing other literature and material, as necessary. Facilitates correct understanding of the material. This activity also includes the time required for review before exams.
5h 30min
Assessment
4 Quiz activities
Quiz activities meant to provide quick, unassessed feedback to students regarding their grasp of the material.
30 min

 

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