QBUS3820 Lecture Notes - Lecture 1: Software Engineering, No Free Lunch Theorem, Unsupervised Learning

• School: University of Sydney
• Department: Business Analytics
• Course: QBUS3820
• Professor: Peter

Document Summary

Discipline of business analytics, the university of sydney business school. Lecture 1: introduction to machine learning & data mining: introduction, business examples, notation, statistical decision theory, evaluating model performance, overview of some key concepts and themes. Machine learning is a set of methods for automatically detecting patterns in data and using them for predicting future data and guiding decision making. We can think of statistical learning as a framework for machine learning that draws on statistics. Two trends bring statistical learning to the forefront of successful business decision making: we are in the era of big data. There are two main types of learning: in predictive or supervised learning, the objective is to learn a function to predict an output variable y based on observed input variables x1, . , xp: in descriptive or unsupervised learning, we only have inputs, x1, . , xp, and the goal is to nd interesting patterns in this data.