Machine Learning & Pattern Recognition
Machine learning (ML) and pattern recognition are closely related fields that are concerned with teaching machines to recognize patterns in data and make predictions or decisions based on those patterns.
Machine learning is a subfield of artificial intelligence (AI) that involves training algorithms to automatically learn from data without being explicitly programmed. ML algorithms are trained on large datasets and can identify patterns in the data that can be used to make predictions or classifications.
Pattern recognition, on the other hand, is a field of study that involves the identification of patterns in data. Pattern recognition techniques can be used to identify regularities in data, such as recurring shapes or sequences, and classify data based on those patterns.
Both ML and pattern recognition involve the use of statistical and mathematical techniques to identify patterns in data. However, while pattern recognition typically involves the use of pre-defined rules and algorithms, ML algorithms are designed to learn from the data and adjust their behavior accordingly.