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.
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.
The rationale and vision behind julia, and its design principles are discussed in this session.
DataFrames is one of the most widely used Julia packages. This session is an introduction to data analysis with Julia using DataFrames.
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.
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 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.
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.