| Topic | Assessment |
The participant is able to explain basic concepts ...
(1) |
The participant understands and is able to explain...
(1) |
The participant is able to assess where and how ma...
(1) |
The participant is able to apply machine learning ...
(1) |
The participant is able to identify machine learni...
(1) |
|
|---|---|---|---|---|---|---|---|
| Formative | Summative | ||||||
| M1-INTRO: Introduction to Artificial Intelligence and Machine Learning | 0 | 0 | |||||
| M2-DATA-ANALYSIS: The Data Analysis Process | 0 | 0 | |||||
| M3-SIMPLE-ML: Introduction to Simple Machine Learning Methods | 0 | 0 | |||||
| M4-CLUST: Cluster Analysis | 0 | 0 | |||||
| M5-CONVEX-OPTI: Convex Optimization | 0 | 0 | |||||
| M6-OPTI-LEARN: Optimization-based Machine Learning | 0 | 0 | |||||
| M7-EVAL: Evaluating Model Performance | 0 | 0 | |||||
| M8-INTERPRET-TABULAR: Interpretability of Models on Tabular Data | 0 | 0 | |||||
| M9-AUTODIFF-ANN: Introduction to Neural Networks and Automatic Differentiation | 0 | 0 | |||||
| M10-DEEP-LEARN: Deep Learning | 0 | 0 | |||||
| M11-DEEP-LEARN-ADVANCED: Advanced Approaches in Deep Learning | 0 | 0 | |||||
| M12-INTERPRET-DEEP: Interpretability Methods for Deep Learning | 0 | 0 | |||||
| M13-DEEP-LEARN-SEQ: Deep Learning for Sequential Data | 0 | 0 | |||||
| M14-ENSEMBLE: Ensemble Methods | 0 | 0 | |||||
| M15-DIMRED: Dimensionality Reduction | 0 | 0 | |||||
| M16-EMBED: Embeddings | 0 | 0 | |||||
| M17-GP-HYPEROPT: Gaussian Processes and Hyperparameter Optimization | 0 | 0 | |||||
| M18-RL: Reinforcement Learning | 0 | 0 | |||||
| M19-DEEP-RL: Deep Reinforcement Learning | 0 | 0 | |||||
| M20-SVM: Support Vector Machines | 0 | 0 | |||||
| M21-SEARCH: Search Methods | 0 | 0 | |||||
| M22-ADVERSARIAL-SEARCH: Search Methods in Adversarial Contexts | 0 | 0 | |||||
| M23-METAHEURISTICS: Metaheuristic Optimization | 0 | 0 | |||||
| M24-STATE-SPACE: State-space Approaches in Control | 0 | 0 | |||||
| M25-BAYES-NET: Bayesian Networks | 0 | 0 | |||||
| M26-GAMING: AI and Gaming | 0 | 0 | |||||
| M27-FAIRNESS: Fairness in Machine Learning | 0 | 0 | |||||
| Team Project | 0 | 0 | |||||
| Total | 0 | 0 | 0% | 0% | 0% | 0% | 0% |
| 0 | |||||||
