1.) OF KNOWLEDGE:
- Know the different tasks that can be solved with machine learning
- Know machine learning techniques and their typology
- Know the methodology of machine learning and the phases it entails
- Know tools available for machine learning
- Understand the fundamentals and motivations of machine learning
- Understand the work methodology and the different phases of machine learning
- Understand the usefulness of different machine learning techniques
- Understand the relationship between model complexity, amount of data, problem characteristics and overlearning
- Analyze the domains and design knowledge extraction processes according to the problem.
- Evaluate the performance and efficiency of the different machine learning methods
- Work on specific domains and contrast different techniques to check their performance in machine learning
4.) CRITICISM OR ASSESSMENT
- Selection of algorithms, selection of models and adjustment of parameters.
- Consider the relationship between computational cost and marginal improvement of different solutions
- Assessment of whether the results obtained are adequate, compared with chance or basic algorithms