Intended learning outcomes *
Having passed the course, the student should be able to:
1. solve problems independently
a. take part in the literature about learning machines and account for their role both historically and today, and in the future, b. relate own models to already existing research and development in the area of learning machines,
2. master abstraction
a. define what a learning machine is and is not,.b. identify relevant concepts and applicable methods and tools,
c. associate critically different relevant concepts and phenomena with learning machines,
d. instrumentalise relevant abstract concepts
3. implement learning machines
a. use tools to build own learning machines, as well as analyse others', b. program, test and critically evaluate own software for learning machines,
c. estimate the correctness and the computational complexity in programs for learning machines.
in order to
- gain knowledge of the different ways in which machine learning methods can be combined to be enclosed in physical or abstract models, so-called learning machines,
- be able to discuss how difficult problems are best solved by means of learning machines,
- be able to evaluate which problems, which overhead costs and which meta learning tools that should be used
- obtain an approach to the subject that admits ethical perspectives in which general artificial intelligence is included.
For higher grades, the student should also be able to
- analyse statistical disturbance factors (confounders), over fitting as well as generalizability in own solutions based on learning machines,
- carry out self critical review of own programming of learning machines including ethical perspectives and sustainability perspectives, as well as to document the same.
- master the meta level through modelling of different solutions based on learning machines, that is to be able to talk about these using adequate terminology.