Answer : D
a. Supervised learning 3. Manual labels of inputs are used
b. Unsupervised learning 2. Manual labels of inputs are not used
c. Re-inforcement learning 1. The decision system receives rewards for its action at the end
d. Inductive learning 4. System learns by example
Supervised learning and Unsupervised learning both are machine learning task
Basic difference in supervised learning and unsupervised learning is In supervised learning, the output datasets are provided which are used to train the machine and get the desired outputs whereas in unsupervised learning no datasets are provided, instead the data is clustered into different classes .
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
Inductive Learning is a powerful strategy for helping students deepen their understanding of content and develop their inference and evidence-gathering skills. In an Inductive Learning lesson, students examine, group, and label specific "bits" of information to find patterns.
If we are so fortunate we might learn all these terms in our Master.