Decision trees can suffer from high variance which makes their results fragile to the specific training data used. Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated. Random Forest is an extension of bagging that in addition to building trees based on multiple samples of your training data, it also constrains the features that can be used to build the trees, forcing trees to be different. This, in turn, can give a lift in performance. In this tutorial, you will discover how to implement the Random Forest algorithm from scratch in Python.

In this talk we will present some techniques that we use on a day to day basis in our research, where we combine our internet-wide data scanning and acquisition platform with ML/Data science techniques which allows us to find things faster or extract results in a more automated way. We will focus on practical cases and examples that even our audience at home will be able to use if they want. A couple of examples we will look at is how to classify images such as VNC screenshots, we will look at network scans and using machine learning to classify them and also the use of natural language processing to analyze CVEs. We will also talk a bit about a data analysis and classification pipeline architecture, we will look at the different technologies and what they do and how they can be used. We will start by giving a very brief entry to the data science world and talk about: Technologies Techniques How these relate to infosec Algorithms and how they can be used How people can come into the world of data and machine learning Data visualization techniques and what are the best choices for different types of data A couple of examples we will look at is how to classify images such as VNC or x11 screenshots, OCR, we will look at network scans and using machine learning to classify them and also the use of natural language processing to analyze CVEs.

Revenue was up 54% from last year and beat analysts' expectations by over $300 million. Nvidia used to be a little company making graphics chips for PCs, but it's well on the way to transforming into one of the leading computing platforms for cloud servers, machine learning, and artificial intelligence. Fortunately for Nvidia, it turns out that the kinds of tasks graphics chips are good at--like processing many, many simple calculations at the same time--are just what's needed to run analysis programs in a cloud data center, steer a self-driving car, or pilot an automated drone. Thursday brought more evidence that the company's successful transition is in full swing. Nvidia NVDA reported third quarter results that blew through Wall Street expectations, and its stock price, which had already doubled this year, rose another 15% in after-hours trading.

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It's the same algorithm but it's fed different training data so it comes up with different classification logic. "Machine learning" is an umbrella term covering lots of these kinds of generic algorithms.

Artificial intelligence and its machine learning subset are all the rage these days. That was evident when I spoke this week at the AI World event, which was packed with vendors and users seeking to understand what the hell AI and machine learning are--and wanting to know how they could use this old but revitalized technology effectively. Amazon Web Services, Google, IBM, Microsoft, and the other major cloud providers all have machine learning services in their clouds now. But most enterprises have no clue on what the heck to do with machine learning systems, whether cloud or on-premises. It is critical to find the right uses for machine learning.

Symantec has launched Endpoint Protection 14, a new security solution which harnesses artificial intelligence to protect clients. Announced on November 1, the new security offering is powered by AI and machine learning on the endpoint and in the cloud. Symantec says that by harnessing machine learning to collate data and detect patterns and anomalies which may indicate a cyberattack, AI provides "a multi-layered solution able to stop advanced threats and respond at the endpoint regardless of how the attack is launched." Symantec Endpoint Protection combines machine learning, memory exploit mitigation, and threat intelligence provided by Symantec and Blue Coat, which combined their research and security operations in October after Symantec completed the acquisition of Blue Coat for $4.6 billion. The company also says that the solution is capable of 99.9 percent efficacy, low false positives, and a 70 percent carbon footprint reduction in comparison to past endpoint software.

IBM Research have created an open-source artificial intelligence robot called TJ Bot. Downloading the source via GitHub – coders can 3D print or Laser Cut the template, attach a Raspberry Pi and integrate it with IBM Watson API's. These include Watson Tone Analyzer, Watson Speech to Text API and Watson Conversation. TJ Bot is an example of'embodied cognition' – the idea of embedding artificial intelligence into objects in our everyday lives.

Borland theorises that, just as has been the case with waves of automation since the industrial revolution, technology is creating jobs as fast as it is eradicating them. New digital technologies have increased demand for software designers, programmers and managers, while lower costs of production from automation have resulted in higher real incomes that in turn lead to more demand and more jobs. But there is one difference. This time, big data and advances in computerisation mean technology is no longer replacing low-skilled blue collar jobs.

Deep Learning can be described as a new machine learning toolkit that has a high likelihood to lead to more advanced forms of artificial intelligence. The evidence for this is in the sheer number of breakthroughs that had occurred since the beginning of this decade. There is a new found optimism in the air and we are now again in a new AI spring. Unfortunately, the current state of deep learning appears to many ways to be akin to alchemy. Everybody seems to have their own black-magic methods of designing architectures.

SAP acquired procurement software vendor Ariba in 2011, but the company's history dates back two decades. This week, SAP Ariba executives briefed analysts in Boston, giving an overview of recent roadmap milestones as well as a look ahead at what's yet to come. Growth markers: There are now 2.4 million suppliers on Ariba's business network, with more than $1 trillion in commerce transactions each year. In addition, Ariba has a presence in 190 countries. Yet Ariba has set some lofty goals for additional growth.