While there are many online courses to learn Python for Machine learning and Data science, books are still the best way to for in-depth learning and significantly improving your knowledge. Python is a universal language that is used by both data engineers and data scientists and probably the most popular programming language as well. All the Data Scientists I have spoken and many in my friend circle just loves Python, mainly because it can automate all the tedious operational work that data engineers need to do. To make the deal even sweeter, Python also has the algorithms, analytics, and data visualization libraries like Metaplotlib, which is essential data scientists. In both roles, the need to manage, automate, and analyze data is made easier by only a few lines of code.

Matt Beane, assistant professor at the University of California, Santa Barbara, finds that robots, machine learning, and AI are changing how we train for our jobs -- not just how we do them. His study shows that robot-assisted surgery is disrupting the traditional learning pathway of younger physicians. He says this trend is emerging in many industries, from finance to law enforcement to education. And he shares lessons from trainees who are successfully working around these new barriers. Beane is the author of the HBR article "Learning to Work with Intelligent Machines." CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. Like it or not, people increasingly do their jobs with robots, machine learning, or artificial intelligence. These developing technologies are already destroying some jobs and changing how many others are performed. But it's not just work that's changing.

Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore, efficient algorithms for solving such SOECs need to limit the fraction of data points that they use, which we refer to as algorithmic data complexity. Recent stochastic first order methods exhibit low data complexity when handling SOECs but guarantee near-feasibility and near-optimality only at convergence. These methods may thus return highly infeasible solutions when heuristically terminated, as is often the case, due to theoretical convergence criteria being highly conservative. This issue limits the use of first order methods in several applications where the SOEC constraints encode implementation requirements. We design a stochastic feasible level set method (SFLS) for SOECs that has low data complexity and emphasizes feasibility before convergence. Specifically, our level-set method solves a root-finding problem by calling a novel first order oracle that computes a stochastic upper bound on the level-set function by extending mirror descent and online validation techniques. We establish that SFLS maintains a high-probability feasible solution at each root-finding iteration and exhibits favorable iteration complexity compared to state-of-the-art deterministic feasible level set and stochastic subgradient methods. Numerical experiments on three diverse applications validate the low data complexity of SFLS relative to the former approach and highlight how SFLS finds feasible solutions with small optimality gaps significantly faster than the latter method.

High concentration agricultural facilities such as vertical farms or plant factories considers hydroponic techniques as optimal solutions. Although closed-system dramatically reduces water consumption and pollution issues, it has ion-ratio related problem. As the root absorbs individual ions with different rate, ion rate in a nutrient solution should be adjusted periodically. But traditional method only considers pH and electrical conductivity to adjust the nutrient solution. So ion imbalance and accumulation of excessive salts. To avoid those problems, some researchers have proposed ion-balancing methods which measure and control each ion concentration. However, those approaches do not overcome the innate limitations of ISEs, especially ion interference effect. An anion sensor is affected by other anions, and the error grows larger in higher concentration solution. A machine learning approach to modify ISE data distorted by ion interference effect is proposed in this paper. As measurement of TDS value is relatively robust than any other signals, we applied TDS as key parameter to build a readjustment function to remove the artifact. Once a readjustment model is established, application on ISE data can be done in real time. Readjusted data with proposed model showed about 91.6~98.3% accuracies. This method will enable the fields to apply recent methods in feasible status.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

Talking about acts of violence like mass shootings with your children is not easy. If you have to have that difficult talk, remember the four S's. Video games again have been invoked as one of the causes of violence in the U.S. in the wake of mass shootings this weekend in El Paso, Texas, and Dayton, Ohio. President Donald Trump, who last year held a video game summit after the February 2018 Parkland, Florida, shooting that killed 17 people at Marjory Stoneman Douglas High School, was among several public officials who called out video games as a potential factor in shootings, mentioning video games and violence. President Donald Trump on Monday condemned white nationalism and said he supported "red flag" laws, which could limit a person's access to firearms if the person is determined to be a potential threat to the public.

The University of North Texas (UNT) Libraries in partnership with the University of Illinois at Chicago were awarded a National Leadership Grant (IMLS:LG-71-17-0202-17) from the Institute of Museum and Library Services (IMLS) to research the efficacy of using machine-learning algorithms to identify and extract content-rich publications contained in web archives. With the increase of institutions that are collection web-published content into web archives, there has been growing interest in mining these web archives to extract publications or documents that align with existing collections or collection development policies. These identified publications could then be integrated into existing digital library collections where they would become first-order digital objects instead of content accessible only to discovery by traversing the web archive or though a well crafted full text search. This project is focusing on the first piece of this workflow, to identify the publications that exist and separate them from content that does not align with existing collections. To operationalize this research, the project is focusing on three primary use cases, including: extracting scholarly publications for an institutional repository from a university domain's web archive (unt.edu

I started 2019 by saying it would be the year of AI. As we witness how this game-changing technology is dominating the agendas of leading organisations and nations, I can say I was right. At Microsoft, we have helped organisations in Thailand harness artificial intelligence as the core of their digital transformation strategy, offering them a competitive advantage. Whether employed to improve operational efficiency or create entirely new business models, making AI part of your core strategy translates into growth and added value. To help understand how AI is shaping the future for Thai businesses, Microsoft partnered with the research firm IDC to survey readiness for AI adoption among business leaders and workers.

This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fixing the learning algorithm used and focusing only on the impact of the different exploration bonuses in the agent's performance. We use Rainbow, the state-of-the-art algorithm for value-based agents, and focus on some of the bonuses proposed in the last few years. We consider the impact these algorithms have on performance within the popular game Montezuma's Revenge which has gathered a lot of interest from the exploration community, across the the set of seven games identified by Bellemare et al. (2016) as challenging for exploration, and easier games where exploration is not an issue. We find that, in our setting, recently developed bonuses do not provide significantly improved performance on Montezuma's Revenge or hard exploration games. We also find that existing bonus-based methods may negatively impact performance on games in which exploration is not an issue and may even perform worse than $\epsilon$-greedy exploration.

The ability to generate appropriate verbal and non-verbal backchannels by an agent during human-robot interaction greatly enhances the interaction experience. Backchannels are particularly important in applications like tutoring and counseling, which require constant attention and engagement of the user. We present here a method for training a robot for backchannel generation during a human-robot interaction within the reinforcement learning (RL) framework, with the goal of maintaining high engagement level. Since online learning by interaction with a human is highly time-consuming and impractical, we take advantage of the recorded human-to-human dataset and approach our problem as a batch reinforcement learning problem. The dataset is utilized as a batch data acquired by some behavior policy. We perform experiments with laughs as a backchannel and train an agent with value-based techniques. In particular, we demonstrate the effectiveness of recurrent layers in the approximate value function for this problem, that boosts the performance in partially observable environments. With off-policy policy evaluation, it is shown that the RL agents are expected to produce more engagement than an agent trained from imitation learning.