tatistical earning the purpose of this is to introduce the reader to some common concepts and themes in statistical learning we discuss the difference between supervised and unsupervised learningand how we can assess the predictive performance of supervised learning we also examine the central role that the linear and gaussian properties play in the modeling of data we conclude with section on bayesian learning the required probability and statistics background is given in appendix introduction although structuring and visualizing data are important aspects of data sciencethe main challenge lies in the mathematical analysis of the data when the goal is to interpret the model and quantify the uncertainty in the datathis analysis is usually referred to as statistical learning in contrastwhen the emphasis is on making predictions using large-scale datathen it is common to speak about machine learning or data mining there are two major goals for modeling data to accurately predict some future quantity of interestgiven some observed dataand to discover unusual or interesting patterns in the data to achieve these goalsone must rely on knowledge from three important pillars of the mathematical sciences function approximation building mathematical model for data usually means understanding how one data variable depends on another data variable the most natural way to represent the relationship between variables is via mathematical function or map we usually assume that this mathematical function is not completely knownbut can be approximated well given enough computing power and data thusdata scientists have to understand how best to approximate and represent functions using the least amount of computer processing and memory optimization given class of mathematical modelswe wish to find the best possible model in that class this requires some kind of efficient search or optimization procedure the optimization step can be viewed as process of fitting or calibrating function to observed data this step usually requires knowledge of optimization algorithms and efficient computer coding or programming statistical learning machine learning data mining