By: Julia Developers

Re-posted from: http://feedproxy.google.com/~r/JuliaLang/~3/mHKnjWXt3KA/julia-tutorial-MIT

We held a two day Julia tutorial at MIT in January 2013, which included 10 sessions. MIT Open Courseware and MIT-X graciously provided support for recording of these lectures, so that the wider Julia community can benefit from these sessions.

## Julia Lightning Round (slides)

This session is a rapid introduction to julia, using a number of lightning rounds. It uses a number of short examples to demonstrate syntax and features, and gives a quick feel for the language.

## Rationale behind Julia and the Vision (slides)

The rationale and vision behind julia, and its design principles are discussed in this session.

## Data Analysis with DataFrames (slides)

DataFrames is one of the most widely used Julia packages. This session is an introduction to data analysis with Julia using DataFrames.

## Statistical Models in Julia (slides)

This session demonstrates Julia’s statistics capabilities, which are provided by these packages: Distributions, GLM, and LM.

## Fast Fourier Transforms

Julia provides a built-in interface to the FFTW library. This session demonstrates the Julia’s signal processing capabilities, such as FFTs and DCTs. Also see the Hadamard package.

## Optimization (slides)

This session focuses largely on using Julia for solving linear programming problems. The algebraic modeling language discussed was later released as JuMP. Benchmarks are shown evaluating the performance of Julia for implementing low-level optimization code. Optimization software in Julia has been grouped under the JuliaOpt project.

## Metaprogramming and Macros

Julia is homoiconic: it represents its own code as a data structure of the language itself. Since code is represented by objects that can be created and manipulated from within the language, it is possible for a program to transform and generate its own code. Metaprogramming is described in detail in the Julia manual.

## Parallel and Distributed Computing (Lab, Solution)

Parallel and distributed computing have been an integral part of Julia’s capabilities from an early stage. This session describes existing basic capabilities, which can be used as building blocks for higher level parallel libraries.

## Networking

Julia provides asynchronous networking I/O using the libuv library. Libuv is a portable networking library created as part of the Node.js project.

## Grid of Resistors (Lab, Solution)

The Grid of Resistors is a classic numerical problem to compute the voltages and the effective resistance of a 2n+1 by 2n+2 grid of 1 ohm resistors if a battery is connected to the two center points. As part of this lab, the problem is solved in Julia in a number of different ways such as a vectorized implementation, a devectorized implementation, and using comprehensions, in order to study the performance characteristics of various methods.