Introduction to Machine Learning in Julia

Machine learning is a branch of AI which is based on feeding the data to the system, identifying the pattern, and making the decision without any explicit intervention. The term Machine Learning was coined by Arthur Samuel in 1959, an American pioneer in the field of computer gaming and artificial intelligence, and stated that “it gives computers the ability to learn without being explicitly programmed”.  Machine learning is also related to computational statistics which is the main task to make the machine predict the outcome accurately. 

Relationship to the other fields

• Artificial intelligence: Machine learning is often used to program a machine in such a way that after a certain training on the particular dataset it makes its decision itself without the human intervention.
• Data mining: Data mining focuses on knowing the properties of dataset features using Machine learning methods. On the other hand, machine learning uses data mining as an unsupervised learning method.
• Optimization: Algorithm can minimize the loss on the training dataset while machine learning can minimize the loss on the unseen or other datasets.
• Statistics: Statistics draws population inferences from a sample, while machine learning finds generalization predictive patterns.

There are several algorithms available to work with certain datasets with conditions, however, when there are big data where millions of data are available these algorithms' efficiency reduces. Basic idea was to train the system on data-set for learning and then allowing it to make its own decision according to the situation. e.g In 1997, Tom Mitchell defined machine learning that “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.

Julia as a Programming Language

Now, Julia was first launched in 2012 by Viral B. Shah, Jeff Bezanson, Stefan Karpinski, and other contributors at Massachusetts Institute of Technology (MIT), USA. It is an open-source language and widely implemented in statistical computing, scientific research, data analytics, graphical representation, data modeling, and reporting. It has all the libraries needed for data science as well. 

Industry implementations of Julia

Few of the Fortune 500 companies, like BlackRock, uses Julia programming language for their time-series analysis. Another British organization called Aviva uses Julia as risk calculations. Reserve Bank of New York used to make models of the United States economy, noting that the language made model estimation using Julia. According to the organization, the model is 10 times faster than its previous MATLAB implementation. 

Recently NVIDIA has implemented Julia in their GPU's as they find Julia easy to use like python but run like C. It adds the high performance and multiple threads functioning more. Use a large community of machine learning experts to build their high performing models. 

Popular Julia Packages for Machine Learning

• Mocha.jl: This is a deep learning Package written in Julia. It is a native interface that can interact with the core functionalities. with this package, there is no need for including the external dependencies.
• Knet: This is a deep learning package written in Julia. It allows models to be defined their computations in Julia, use of loops, recursion, concatenation, and other features.
• SciKitLearn.jl: It is a wrapper written in Julia for the python library known as Scikitlearn.
• Flux: Flux is a package in Julia which intuitively define the model, same as a mathematical notation.  Any existing Julia library can be directly included in the flux models.
• Merlin: This is a deep learning package written in Julia. It provides a fast and compact library for machine learning. The library runs on CPUs and CUDA GPUs.
• MLBase.jl: Julia package provides tools that support programs like data pre-processing and modification, classification, evaluation, validation, and model-tuning.
• Strada: it is a Julia package that has Caffe framework. It supports Caffe features and integration with Julia.
• TensorFlow.jl: It is a wrapper for TensorFlow of open-source.

Application of Julia in Machine learning

Currently, Julia in Machine learning mainly focuses on computer vision, Internet of things(IOT), Graph analytics, signal processing, Natural language processing.

• Computer vision: It is a biological vision using a computer and related input/output devices. Scholars and engineers developed packages for the applications of computer vision like Metalhead.jl, Flux and ImageProjectiveGeometry.jl
• Natural language processing: computational techniques for making machine learning, understanding, and producing human language content. Package LightNLP.jl is a lightweight NLP toolkit for Julia.
• Graph analytics: This is to incorporate graph, statistics and database technology to model, store, retrieve, and analyze graphs. Julia has a package like LightGraphs.jl
• Internet of things(IOT) is a paradigm as a network of machines and devices connected to each other. Fugro Roames engineers have used Julia in the machine learning algorithm to identify network faults and potential faults.

Types of Machine Learning Methods

• Regression: The regression technique helps the machine learning approach to predict continuous values. For example, detecting the correct image
• Classification: The input is divided into one or more classes or categories for the learner to produce a model to assign unseen modules. For example, in the case of email unauthentic, we can divide the emails into two classes i.e “spam” and “not spam”.
• Clustering: This technique follows the summarizing, finding a group of similar entities. For example, we can gather and take readings of the patients in the hospital.
• Association: This technique finds co-occurring events or items. For example, market-basket.
• Anomaly Detection: This technique works by discovering abnormal cases or behavior. For example, credit card fraud detection.
• Sequence Mining: This technique predicts the next stream event. For example, click-stream event.
• Recommendation: This technique recommends the item. For example, songs or movies according to the celebrity in it.

Advantages of implementing Machine learning Using Julia

• Unlike Python, Julia is complied like C language but not interpreted. This makes the code run ability faster.
• It is also designed in such a way that it can also work well with parallel and distributed computing.
• The code of Julia is straight compiled rather than interpreted, which makes it faster.
• It has a built-in package manager.
• The function call is direct. There are no wrappers or container API need for it.
• User-defined types are as fast and compact as built-ins.
• It comes with its own command-line tool.
• It has easy to use math friendly Syntax.
• Julia is open source and holds free under MIT license.
• It is extensible.
• Works great with GPU's and CPU's

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• Julia
• Julia-Machine-learning