It was his unique teaching style that got me and a bunch of my friends hooked to this topic and field – his enthusiasm towards the material, the intuitive examples that he gives…,

Subtitles are available for Introduction, Case Study and Design Patterns Concepts. Design and architecture of any software/solution provides the base and makes it flexible, extensible for future requirements. A good designed software/solution makes it easy to understand and maintain. Design patterns are known as best practices to design software for problems which are repeated in nature. This course "Design Patterns Through Python" gives you understanding of all 23 patterns described in Gang Of Four book - "Design Patterns: Elements of Reusable Object-Oriented Software", by Gamma, Helm, Johnson and Vlissides.

Skövde is a municipality in central Sweden. When the pupils in four of their primary schools come back after the summer holidays a new educational assistant will meet up. Since the municipality has bought four robots to the schools. The aim is that the robots will assist in class in different ways, e.g. to help pupils that are lagging behind. The pioneering efforts to include robot assistants in education is triggered by Skövde University College with front-line research and development within a range of technologies, like artificial intelligence and cognitive and interactive systems.

Have you ever wanted to build a games with a graphical interface but didn't know how to? May be you even know how to create tools on a command line but have no idea how to convert it into a graphical interface that people can click on. In this course we will be learning Python GUI Programming Turtle other advanced python modules to build graphical user interfaces (GUI) and games from scratch. We will learn from basics of Python i.e. variables, slicing, string, some module, arithmetic and logical operations, looping, functions, object oriented programming. After that we will learn the basics stuff of Pygame and OpenGL and Blender basics stuff.

Ganesh Bell, President, Uptake Stanford Graduate School of Business video above published May 24, 2018: Uptake (domain: uptake.com) is supplying software for the infrastructure that powers the world, said Uptake President, Ganesh Bell. The company is shaping a whole new technical stack for the digital industrial transformation. "Industrial companies previously operated with data that was predicated on physics-based analytics. But now machine learning and cloud technologies can really provide truly predictive analytics in ways which previously could not be done." During his visit to the Systems Leadership class on May 17, 2018, Bell discussed with Lecturer Robert Siegel how Uptake is developing software to work with industrial companies to deliver better customer outcomes.

We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.

As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying. This paper addresses these challenges by presenting: a) a comprehensive theoretical analysis of variance reduced zeroth-order (ZO) optimization, b) a novel variance reduced ZO algorithm, called ZO-SVRG, and c) an experimental evaluation of our approach in the context of two compelling applications, black-box chemical material classification and generation of adversarial examples from black-box deep neural network models. Our theoretical analysis uncovers an essential difficulty in the analysis of ZO-SVRG: the unbiased assumption on gradient estimates no longer holds. We prove that compared to its first-order counterpart, ZO-SVRG with a two-point random gradient estimator could suffer an additional error of order $O(1/b)$, where $b$ is the mini-batch size. To mitigate this error, we propose two accelerated versions of ZO-SVRG utilizing variance reduced gradient estimators, which achieve the best rate known for ZO stochastic optimization (in terms of iterations). Our extensive experimental results show that our approaches outperform other state-of-the-art ZO algorithms, and strike a balance between the convergence rate and the function query complexity.

This document contains the outcome of the first Human behaviour and machine intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona, Spain. The workshop was organized in the context of a new research programme at the Centre for Advanced Studies, Joint Research Centre of the European Commission, which focuses on studying the potential impact of artificial intelligence on human behaviour. The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs. The document is made of short position statements and identification of challenges provided by each expert, and incorporates the result of the discussions carried out during the workshop. In the conclusion section, we provide a list of emerging research topics and strategies to be addressed in the near future.

A large proportion of online comments present on public domains are constructive, however a significant proportion are toxic in nature. The comments contain lot of typos which increases the number of features manifold, making the ML model difficult to train. Considering the fact that the data scientists spend approximately 80% of their time in collecting, cleaning and organizing their data [1], we explored how much effort should we invest in the preprocessing (transformation) of raw comments before feeding it to the state-of-the-art classification models. With the help of four models on Jigsaw toxic comment classification data, we demonstrated that the training of model without any transformation produce relatively decent model. Applying even basic transformations, in some cases, lead to worse performance and should be applied with caution.

Commonsense reasoning is a longstanding challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset [1]. In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.