Technology firm Rose India announces online machine learning course to teach TensorFlow 2.0 using Python programming language. This training course is intended to provide enough knowledge to the students on the fast track to help them in mastering newly released TensorFlow 2.0 mathematical computing framework. Machine learning is the application of mathematics, data analytics, data processing, programming and other field of science for development of program (called model) which automatically decide based on the data it receives. Machine learning in the IT industry relies on the application of software algorithms mostly the mathematical solution to perform tasks of data analysis and prediction. In machine learning software developer uses various mathematical algorithms for programming a machine that can learn from data.

Artificial Intelligence (AI) promises to change each and every aspect of human society. Be it in the form of automatic parking systems, mobile check deposits, social media feeds or countless other technologies that we interact with on a daily basis – Artificial Intelligence is practically everywhere. And pretty soon, it will completely reshape the academic world. Already, educational procedures globally have transformed to integrate different applications of AI. With learning material accessible through smartphones and tabs – students now don't have to rely on conventional books.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.

In this paper, we introduce the increasing belief criterion in association rule mining. The criterion uses a recursive application of Bayes' theorem to compute a rule's belief. Extracted rules are required to have their belief increase with their last observation. We extend the taxonomy of association rule mining algorithms with a new branch for Bayesian rule mining~(BRM), which uses increasing belief as the rule selection criterion. In contrast, the well-established frequent association rule mining~(FRM) branch relies on the minimum-support concept to extract rules. We derive properties of the increasing belief criterion, such as the increasing belief boundary, no-prior-worries, and conjunctive premises. Subsequently, we implement a BRM algorithm using the increasing belief criterion, and illustrate its functionality in three experiments: (1)~a proof-of-concept to illustrate BRM properties, (2)~an analysis relating socioeconomic information and chemical exposure data, and (3)~mining behaviour routines in patients undergoing neurological rehabilitation. We illustrate how BRM is capable of extracting rare rules and does not suffer from support dilution. Furthermore, we show that BRM focuses on the individual event generating processes, while FRM focuses on their commonalities. We consider BRM's increasing belief as an alternative criterion to thresholds on rule support, as often applied in FRM, to determine rule usefulness.

Reinforcement learning (RL) continues to be less valuable for business applications than supervised learning, and even unsupervised learning. It is successfully applied only in areas where huge amounts of simulated data can be generated, like robotics and games. However, many experts recognize RL as a promising path towards Artificial General Intelligence (AGI), or true intelligence. Thus, research teams from top institutions and tech leaders are seeking ways to make RL algorithms more sample-efficient and stable. We've selected and summarized 10 research papers that we think are representative of the latest research trends in reinforcement learning. The papers explore, among others, the interaction of multiple agents, off-policy learning, and more efficient exploration.

You are invited to submit papers for the PEARC20 Conference – Catch the wave that will be held in Portland, July 26–30, 2020. Presentations may address any topic related to advanced research computing, but topics consistent with one or more of the following four technical tracks are of particular interest. Proposals may take several forms as indicated below. Advanced research computing environments – systems and system software: Practice and experience relating to the system (computing hardware, storage hardware, visualization hardware, and network hardware) and system software that drives the "hardware" side of cyberinfrastructure and research computing environments. Examples of relevant topics: GPUs, CPUs, and FPGAs as computational environments; experience with advanced storage systems; hardware and systems for data-centric computing; networking challenges; design and use of visualization environments; funding and operating of advanced research computing facilities (including considerations of cost of locally-sited hardware vs remote hardware such as cloud facilities), systems procurement, systems administration, cybersecurity, practice and experience in facilitating the acquisition, operation, and use of advanced hardware, software, networks and services to securely and sustainably advance research, scholarship, and creativity; performance comparisons and performance aspects of cloud environments.

Deepfakes, or media that takes a person in an existing image, audio recording, or video and replaces them with someone else's likeness using AI algorithms, are multiplying quickly. Amsterdam-based cybersecurity startup Deeptrace found 14,698 deepfake videos on the internet during its most recent tally in June and July, up from 7,964 last December -- an 84% increase within only seven months. That's troublesome not only because deepfakes might be used to sway public opinion during, say, an election, or to implicate someone in a crime they didn't commit, but because the technology has already generated pornographic material and swindled firms out of hundreds of millions of dollars. In an effort to fight deepfakes' spread, Facebook -- along with Amazon Web Services (AWS), Microsoft, the Partnership on AI, Microsoft, and academics from Cornell Tech, MIT, University of Oxford, UC Berkeley; University of Maryland, College Park; and State University of New York at Albany -- are spearheading the Deepfake Detection Challenge, which was announced in September. It's launching globally at the NeurIPS 2019 conference in Vancouver this week, with the goal of catalyzing research to ensure the development of open source detection tools.

Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...

Nuria Oliver is a computer scientist and is also a Fellow of the European Association of Artificial Intelligence and ACM Fellow. She has over 20 years of research experience in the areas of human behaviour modelling and prediction from data and human-computer interaction. Her passion is to improve people's quality of life, both individually and collectively, through technology. Oliver speaks to SciTech Europa Quarterly about the importance of preparing future generations for what's next to come in the world of technology. I think one of the biggest challenges is the profound transformation that will be needed in the educational system and the massive investment in teacher education that would entail.

Greg LaBlanc has been teaching at the Haas School of Business and Berkeley Law since 2005. He teaches primarily in the areas of finance and strategy in the MBA and MFE programs and in Executive Education. He has also worked in competitive intelligence and litigation consulting and has advised consulting teams in finance, marketing, and strategy. His research interests lie at the intersection of law, finance, and psychology, in the area of business strategy and risk management. He is the recipient of teaching awards including the Earl F. Cheit Award for Outstanding Teaching, 2009; and the Haas EWMBA Graduate Instructor of the year, 2004-2005.