Assorted links on learning Machine Learning basics.
2. Introduction to Machine Learning — from The Great Courses Plus
4. Machine Learning (MIT Press Essential Knowledge series)
In machine learning, the aim is to construct a program that fits the given data. A learning program is different from an ordinary computer program in that it is a general template with modifiable parameters, and by assigning different values to these parameters the program can do different things. The learning algorithm adjusts the parameters of the template — which we call a model — by optimizing a performance criterion defined on the data.
The main theory underlying machine learning comes from statistics, where going from particular observations to general descriptions is called inference and learning is called estimation. Classification is called discriminant analysis in statistics. Statisticians used to work on small samples and, being mathematicians, mostly worked on simple models that could be analyzed mathematically. In engineering, classification is called pattern recognition and the approach is more empirical.
Alpaydin, Ethem. Machine Learning (MIT Press Essential Knowledge series)