Supervised Learning
An overarching goal in Machine Learning is to model the distribution of data generated by a process. If there is at least one data item that could be classified as the output of the system, one can then learn the dependency between the output(s) of the system in terms of other data fields generated by the process. This is called supervised learning and most of the practical applications of Machine Learning currently fall into this domain. The majority of interesting problems in Machine Learning are cast either as supervised learning problems or self-supervised learning problems. The spectrum of supervised learning covers problems from credit risk modeling to object classification, from predictive maintenance to predicting protein folding from aminoacid sequences.
Sample Topics
- Machine learning tasks
- Model complexity, generalization, regularization
- Statistical decision theory
- Maximum likelihood estimation and bayesian estimation
- Linear models and additive models
- Mixture models
- Tree-based algorithms
- Kernel methods, Support vector machines
- Artificial neural networks
- Bagging and boosting
- Non-parametric methods
- Hyper-parameter optimization
- Model assessment and selection
- Model inference and ensemble methods
