Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from the observations of its behavior on a task. While this problem has been well investigated, the related problem of {\em online} IRL---where the observations are incrementally accrued, yet the demands of the application often prohibit a full rerun of an IRL method---has received relatively less attention. We introduce the first formal framework for online IRL, called incremental IRL (I2RL), and a new method that advances maximum entropy IRL with hidden variables, to this setting. Our formal analysis shows that the new method has a monotonically improving performance with more demonstration data, as well as probabilistically bounded error, both under full and partial observability. Experiments in a simulated robotic application of penetrating a continuous patrol under occlusion shows the relatively improved performance and speed up of the new method and validates the utility of online IRL.

Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the non-stationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.

Over the years, almost every organization has understood the importance of analyzing data. In fact, it would not be an overstatement to say that "No organization will be able to survive today's cut-throat competition if it does not analyze data." Data analysis as we know it is the process of taking the source data, refining it to get useful information, and then making useful predictions from it. In this Learning Path, we will learn how to analyze data using the powerful toolset provided by Python. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Jack Ma, who is the founder CEO of Internet giant Alibaba, warns that If we do not change the way we teach, 30 years down the line we will be in trouble. The things we teach our children are things from the past 200 years- it's knowledge-based. And we can not teach our kids to compete with machines, that are smarter. This man is a big employer and his assertion on what the employers would need in the near future is credible enough, at least to me. As I said earlier that there's no way we can compete with AI when it comes to efficiency, knowledge, accuracy and incessant replication; how else can we beat it?

Human Resources (HR) have been experiencing significant changes thanks to the evolution of information technologies in the last two decades. Today, Artificial Intelligence (AI) is reshaping the way that companies manage their workforce and make HR plans, which increases productivity and employee engagement in general. Bearing in mind that employee engagement programs boost company revenue by 26%, it is clear that you should accept AI solutions to strengthen your team and gain some long-term benefits. In this article, we will present you 5 ways to use AI in human resources management. AI has the power to take your HR experience to the higher level.

A Montreal program is trying to help get more women working in artificial intelligence. Twenty-eight women, mostly senior undergraduate students, began the six-week AI4Good summer lab on May 14. Almost none of the participants have any experience with AI or machine learning, said Doina Precup, one of the program's creators. The program, now in its second year, aims to give participants the background and the confidence to continue in AI, whether that's by studying it in graduate school, applying the technology in other areas or by starting a business, said Precup, a computer science professor at McGill University and the head of Google-affiliated AI company DeepMind's Montreal lab. In 2017, only 20 per cent of tech workers at Google were women, according to the company's diversity report. Other large tech companies have reported similar numbers.

TensorFlow is quickly becoming the technology of choice for machine learning, because of its ease to develop intelligent machine learning applications and powerful neural networks. If you're a data professional who is familiar with Python and wants to use TensorFlow for performing machine learning activities on a day-to-day basis, then go for this learning path. This comprehensive 2-in-1 course gives you a clear understanding of machine learning models and the application of models at scale using clustering, classification, regression, and reinforcement learning, all with interesting examples and real-world use cases. It's a perfect blend of concepts and practical examples which makes it easy to understand and implement. It follows a logical flow where you will be able to develop efficient and intelligent applications based on your understanding of the different machine learning concepts with every section.

Statistical methods are used at each step in an applied machine learning project. This means it is important to have a strong grasp of the fundamentals of the key findings from statistics and a working knowledge of relevant statistical methods. Unfortunately, statistics is not covered in many computer science and software engineering degree programs. Even if it is, it may be taught in a bottom-up, theory-first manner, making it unclear which parts are relevant on a given project. In this post, you will discover some top introductory books to statistics that I recommend if you are looking to jump-start your understanding of applied statistics. I own copies of all of these books, but I don't recommend you buy and read them all.

Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions). Learn why it's useful and how to approach the problem. There are Rule-Based and ML-Based approaches. The details are really important - training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set.

Machine learning is quite the buzzword these days. While it's been around for a long time, today its applications are wide and far-reaching - from computer science to social science, quant trading and even genetics. From the outside, it seems like a very abstract science that is heavy on the math and tough to visualize. But it is not at all rocket science. Machine learning is like any other science - if you approach it from first principles and visualize what is happening, you will find that it is not that hard.