Artificial behavioral agents are often evaluated based on their consistent behaviors and performance to take sequential actions in an environment to maximize some notion of cumulative reward. However, human decision making in real life usually involves different strategies and behavioral trajectories that lead to the same empirical outcome. Motivated by clinical literature of a wide range of neurological and psychiatric disorders, we propose here a more general and flexible parametric framework for sequential decision making that involves a two-stream reward processing mechanism. We demonstrated that this framework is flexible and unified enough to incorporate a family of problems spanning multi-armed bandits (MAB), contextual bandits (CB) and reinforcement learning (RL), which decompose the sequential decision making process in different levels. Inspired by the known reward processing abnormalities of many mental disorders, our clinically-inspired agents demonstrated interesting behavioral trajectories and comparable performance on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the PacMan game across different reward stationarities in a lifelong learning setting.

This master's thesis work has the objective of performing an analysis of the methods for detecting communities in networks. As an initial part, I study of the main features of graph theory and communities, as well as common measures in this problem. Subsequently, I was performed a review of the main methods of detecting communities, developing a classification, taking into account its characteristics and computational complexity for the detection of strengths and weaknesses in the methods, as well as later works. Then, study the problem of classification of a clustering method, this in order to evaluate the quality of the communities detected by analyzing different measures. Finally conclusions are elaborated and possible lines of work that can be derived.

With deep learning gaining its momentum in fields like self-driving cars, object detection, voice assistants and text generation, to name a few, the demand for deep learning experts in organisations has also significantly increased. As a matter of fact, big tech companies like Facebook, Google, Apple as well as Microsoft have started investing heavily on deep learning projects which, in turn, increase the number of deep learning open jobs in the market. Having said that, deep learning is one of the complex subsets of machine learning and envelops several layers of components which cannot be grasped in a day. Hence, despite the high demand, there is indeed a gap in deep learning talent for organisations. Not only does it come with prerequisites of linear algebra and calculus knowledge but also enough interest to pursue a complicated subject like deep learning.

Rapid Miner is a platform for data scientists and big data analysts to quickly analyse their data. Rapid Miner has taken a huge leap in the AI community since it is most popularly used by non-programmers and researchers. The platform provides a vast number of options in terms of plugins and data analysis techniques. Apart from this, it is also compatible with iOs, Android, and web application tools like Node JS and flask. This platform is useful for anyone with an idea they would like to experiment with without spending much time or effort on it.

Business intelligence (BI) brings a varied collection of strategies that uncover the hidden insights beneath the data sources and convert raw data into intelligent information for business decision making. To stay competitive, businesses must rediscover and use the data they have generated, this makes BI so important. Business intelligence, lets organisations to extract insights from a pool of accessible data to deliver exact, significant, and nearly real-time inputs for decision making. This specialization is offered in collaboration with Tableau, and is aimed for newcomers to data visualization with no prior experience using Tableau. In this course, you will view examples from real world business cases and journalistic examples from leading media companies.

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. HyperOpt is an open-source library for large scale AutoML and HyperOpt-Sklearn is a wrapper for HyperOpt that supports AutoML with HyperOpt for the popular Scikit-Learn machine learning library, including the suite of data preparation transforms and classification and regression algorithms. In this tutorial, you will discover how to use HyperOpt for automatic machine learning with Scikit-Learn in Python. HyperOpt for Automated Machine Learning With Scikit-Learn Photo by Neil Williamson, some rights reserved. HyperOpt is an open-source Python library for Bayesian optimization developed by James Bergstra.

Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020.

This summer, OpenAI, a San Francisco-based artificial intelligence company co-founded by Elon Musk, debuted GPT-3, a powerful new language generator that can produce human-like text. According to Wired, the power of the program, trained on billions of bytes of data including e-books, news articles and Wikipedia (the latter making up just 3% of the training data it used), was producing "chills across Silicon Valley." Soon after its release, researchers were using it to write fiction, suggest medical treatment, predict the rest of 2020, answer philosophical questions and much more.When we asked GPT-3 to write an op-ed convincing us we have nothing to fear from AI, we had two goals in mind.First, we wanted to determine whether GPT-3 could produce a draft op-ed which could be published after minimal editing. Second, we wanted to know what kinds of arguments GPT-3 would deploy in attempting to convince humans that robots come in peace.Here's how we went about it:Step 1: Ask a computer scientist for helpLiam Porr, a computer science student at Berkeley, has published articles written by GPT-3 in the past, so was well-placed to serve as our robot-whisperer.Step 2: Commission the pieceTypically when we commission a human writer, we …

Written for the new generation of hobbyists and aspiring game developers, HTML5 Game Development from the Ground Up with Construct 2 shows you how to use the sophisticated yet user-friendly HTML5-based game engine Construct 2 to develop and release polished, two-dimensional games on a multitude of different platforms. The book also covers the foundational knowledge of game analysis and design based on the author's research and teaching experiences at DigiPen Institute of Technology, James Cook University, and other institutions. The author first helps you understand what really matters in games. He guides you in becoming a better game designer from the ground up, being able to play any game critically, and expressing your ideas in a clear and concise format. The book then presents step-by-step tutorials on designing games. It explains how to build an arcade-style game as well as a platformer integrating some physics elements.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.