What is machine learning (ML)?
• Data is being produced and stored continuously (“big data”):
– science: genomics, astronomy, materials science, particle accelerators. . .
– sensor networks: weather measurements, traffic. . .
– people: social networks, blogs, mobile phones, purchases, bank transactions. . .
• Data is not random; it contains structure that can be used to predict outcomes, or gain knowledge in some way.
Ex: patterns of Amazon purchases can be used to recommend items.
• It is more difficult to design algorithms for such tasks (compared to, say, sorting an array or
calculating a payroll). Such algorithms need data.
Ex: construct a spam filter, using a collection of email messages labelled as spam/not spam.
• Data mining: the application of ML methods to large databases.
• Ex of ML applications: fraud detection, medical diagnosis, speech or face recognition. . .
• ML is programming computers using data (past experience) to optimize a performance criterion.
• ML relies on:
– Statistics: making inferences from sample data.
– Numerical algorithms (linear algebra, optimization): optimize criteria, manipulate models.
– Computer science: data structures and programs that solve a ML problem efficiently.
• A model:
– is a compressed version of a database;
– extracts knowledge from it;
– does not have perfect performance but is a useful approximation to the data.
Examples of ML problems
• Supervised learning: labels provided.
– Classification (pattern recognition):
∗ Face recognition. Difficult because of the complex variability in the data: pose and
illumination in a face image, occlusions, glasses/beard/make-up/etc.
∗ Optical character recognition: different styles, slant. . .
∗ Medical diagnosis: often, variables are missing (tests are costly).