PUBLIC MARKS from ogrisel with tag theory

Course Overview Provides a fundamental understanding of the problems in natural language understanding by computers, and the theory and practice of current computational linguistic systems. Of interest to students of artificial intelligence, algorithms, and the computational processes of comprehension and understanding.

The quest to understand the most complex object in the known universe has been a long and fruitful one. These days we know a good deal about how the human brain works - how our senses translate into electrical signals, how different parts of the brain process these signals, how memories form and how muscles are controlled. We know which brain regions are active when we listen to speech, look at paintings or barter over money. We are even starting to understand the deeper neural processes behind learning and decision-making.

# Balanced treatment of the simplex method and interior-point methods. # Efficient source code (in C) for all the algorithms presented in the text. # Thorough discussion of several interior-point methods including primal-dual path-following, affine-scaling, and homogeneous self dual methods. # Extensive coverage of applications including traditional topics such as network flows and game theory as well as less familiar ones such as structural optimization, L^1 regression, and the Markowitz portfolio optimization model. # Over 200 class-tested exercises. # A dynamically expanding collection of exercises.

There are several good introductions to category theory, each written for a different audience. However, I have never seen one aimed at someone trained as a programmer rather than as a computer scientist or as a mathematician. There are programming languages that have been designed with category theory in mind, such as Haskell, OCaml, and others; however, they are not typically taught in undergraduate programming courses. Java, on the other hand, is often used as an introductory language; while it was not designed with category theory in mind, there is a lot of category theory that passes over directly.

Recent discoveries have unified the fields of computer science and information theory into the field of algorithmic information theory. This field is also known by its main result, Kolmogorov complexity. Kolmogorov complexity gives us a new way to grasp the mathematics of information, which is used to describe the structures of the world. Information is used to describe the cultural structures of science, legal and market institutions, art, music, knowledge, and beliefs. Information is also used in describing the structures and processes of biological phenomena, and phenomena of the physical world. The most obvious application of information is to the engineering domains of computers and communications. This essay will provide an overview of the field; only passing knowledge of computer science and probability theory is required of the reader.

Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called Bayesian reasoning. They sound really enthusiastic about it, too, so you google and find a webpage about Bayes' Theorem and... It's this equation. That's all. Just one equation. The page you found gives a definition of it, but it doesn't say what it is, or why it's useful, or why your friends would be interested in it. It looks like this random statistics thing.