figure - unsupervised learningassociation rule-mining from figure - you can clearly see that based on different customer transactions over period of timewe have obtained the items that are closely associated and customers tend to buy them together some of these frequent item sets are depicted like {meateggs}{milkeggsand so on the criterion of determining good quality association rules or frequent item sets is usually done using metrics like supportconfidenceand lift this is an unsupervised methodbecause we have no idea what the frequent item sets are or which items are more strongly associated with which items beforehand only after applying algorithms like the apriori algorithm or fp-growthcan we detect and predict products or items associated closely with each other and find conditional probabilistic dependencies we cover association rule-mining in further details in semi-supervised learning the semi-supervised learning methods typically fall between supervised and unsupervised learning methods these methods usually use lot of training data that' unlabeled (forming the unsupervised learning componentand small amount of pre-labeled and annotated data (forming the supervised learning componentmultiple techniques are available in the form of generative methodsgraph based methodsand heuristic based methods simple approach would be building supervised model based on labeled datawhich is limitedand then applying the same to large amounts of unlabeled data to get more labeled samplestrain the model on them and repeat the process another approach would be to use unsupervised algorithms to cluster similar data samplesuse human-in-the-loop efforts to manually annotate or label these groupsand then use combination of this information in the future this approach is used in many image tagging systems covering semi-supervised methods would be out of the present scope of this book einforcement learning the reinforcement learning methods are bit different from conventional supervised or unsupervised methods in this contextwe have an agent that we want to train over period of time to interact with specific environment and improve its performance over period of time with regard to the type of actions it performs on the environment typically the agent starts with set of strategies or policies for interacting with the environment on observing the environmentit takes particular action based on rule or policy and by observing the current state of the environment based on the actionthe agent gets rewardwhich could be beneficial or detrimental in the form of penalty it updates its current policies and strategies if needed and