The two terms "supervised learning" and "unsupervised learning" form a related pair of terms. That’s why both are explained here.

Supervised learning: any machine learning process in which the algorithm is somehow steered towards correct results using external resources. In practice, this means that well-curated training data is used for training.

Unsupervised learning: Any machine learning process that does not require prior knowledge to reach its conclusions. In other words: A procedure that is not guided but instead runs independently. The algorithm AlphaGo Zero was NOT fed with training data; the chess computer simply played millions of games against itself and derived strategies for chess from this. For your information: AlphaGo Zero (unsupervised learning) trounced the previous version AlphaGo (supervised learning).

Author

Sebastian Zang has cultivated a distinguished career in the IT industry, leading a wide range of software initiatives with a strong emphasis on automation and corporate growth. In his current role as Vice President Partners & Alliances at Beta Systems Software AG, he draws on his extensive expertise to spearhead global technological innovation. A graduate of Universität Passau, Sebastian brings a wealth of international experience, having worked across diverse markets and industries. In addition to his technical acumen, he is widely recognized for his thought leadership in areas such as automation, artificial intelligence, and business strategy.