This week’s lecture is about uncertainty reasoning. It is part of learning about machine learning.

There are 3 types of machine learning:
– supervised learning
– unsupervised learning
– reinforcement learning

The difference of these types is, supervised means that the classification and learning are supervised by the maker, the maker will help with identifying the things showed to the machine.
Unsupervised means that they will be showed things and will move them in cluster by the similarity of their characteristics.
Reinforcement learning is learning by rewards, they will learn to know whether it is right or wrong by reward and will try to get the reward.

There is also a need for reason probability, to make a rational decision making depending on the situation. There is several rule to probability theory, such as: bayes and naïve bayes.

As for the project, due to our situation of unable to meet with each other, further discussion for the project has not been held.