Association rule learning is a method of machine learning focused on identifying relationships between variables in a database. One example of applied association rule learning is the case where marketers use large sets of super market transaction data to determine correlations between different product purchases. For instance, “customers buying pickles and lettuce are also likely to buy sliced cheese.” Correlations or “association rules” like this can be discovered using association rule learning. Semi-supervised learning is actually the same as supervised learning except that of the training data provided, only a limited amount is labelled. Supervised learning tasks can further be categorized as “classification” or “regression” problems.
A machine learning tool in the hands of an asset manager that focuses on mining companies would highlight this as relevant data. This information is relayed to the asset manager to analyze and make a decision for their portfolio. The asset manager may then make a decision to invest millions of dollars into XYZ stock.
Algorithmic trading and market analysis have become mainstream uses of machine learning and artificial intelligence in the financial markets. Fund managers are now relying on deep learning algorithms to identify changes in trends and even execute trades. Funds and … Read MoreView More Word Embedding with Word2Vec