External Course

Foundations of Data Science - Textbook

Avrim Blum, John Hopcroft, Ravindran Kannan 

June, 2016 

Introduction:

 

Computer science as an academic discipline began in the 1960’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context free languages, and computability. 

 

Introduction to Machine Learning

Machine learning is a subfield of computer science and data science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.

Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Introduction to Graphical Models and Bayesian Networks

Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering -- uncertainty and complexity -- and in particular they are playing an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity -- a complex system is built by combining simpler parts.

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