Machine Learning 1 FER labs
Learning Outcomes:
- Define the basic concepts of machine learning
- Distinguish between generative and discriminative, parametric and nonparametric and probabilistic and nonprobabilistic models models
- Explain the theoretical assumptions, advantages, and disadvantages of basic machine learning algorithms
- Apply model selection and statistical evaluation of the learned model
- Apply various classification algorithms, inclusive generative, discriminative, and nonparametric ones
- Apply clustering algorithms and cluster validation
- Design and implement a machine learning method for classification/clustering and carry out its evalution
- Assess the suitability of a machine learning algorithm for a given task