This paper is a step-by-step tutorial for fitting a mixture distribution to data. It merely assumes the reader has the background of calculus and linear algebra. Other required background is briefly reviewed before explaining the main algorithm. In explaining the main algorithm, first, fitting a mixture of two distributions is detailed and examples of fitting two Gaussians and Poissons, respectively for continuous and discrete cases, are introduced. Thereafter, fitting several distributions in general case is explained and examples of several Gaussians (Gaussian Mixture Model) and Poissons are again provided. Model-based clustering, as one of the applications of mixture distributions, is also introduced. Numerical simulations are also provided for both Gaussian and Poisson examples for the sake of better clarification.

Out of the catalogue of technologies that have the potential to affect the insurance industry, one expert says that artificial intelligence is on track to have the most significant impact in the coming year. In fact, insurers' investments in AI are increasing on a year-over-year basis, and this trend is set to continue into 2019. "AI is important for insurers because it brings them even closer to their customers. To have a firm idea of what AI is and its relevance is now non-negotiable for anyone who considers themselves a market contender in insurance," said Ira Sopic (pictured), project director at Insurance Nexus, which is hosting a live webinar on integrating AI across the insurance organization, including underwriting, customer service, product innovation and claims, as a way to attain efficiency in operations. AI has the power to connect customer data to product information in real-time, enabling better consumer experiences and making the process simpler and faster – ultimately, what customers today want, according to Sopic.

To help fill this void, enterprises are increasingly turning to a tried and true source: higher education. The talent pool for emerging technologies, such as artificial intelligence (AI), machine learning (ML) and internet of things (IoT), will fall short in filling at least 30 percent of global demand, according to IDC. Organizations struggle to hire data scientists and analytics experts who can munge and extract insights from data. "CIOs will also realize that talent shortage will be a moving target driven by supply and demand and they will have to find adaptive, flexible approaches to meet changing needs," IDC analysts wrote in a recent research report. The idea of scouring campuses for tech talent isn't new, but anecdotal evidence suggests that companies are redoubling their efforts to lure -- and help train -- future technologists by partnering with colleges and universities on innovation labs and internship programs aimed at developing real-world digital skills.

Applications can be submitted online at http://www.ai.iitkgp.ac.in/outreach, latest by 15th February, 2019 IIT Hyderabad to launch B.Tech. in Artificial Intelligence Indian Institute of Technology- Kharagpur, Kharagpur-7211302, has invited applications for admission to the Certificate program in'Foundations of Artificial Intelligence and Machine Learning'. The six month course is aimed at working professionals and students. The classes will be conducted in the weekends in Kolkata and Bengaluru from March 2019. Eligibility: Applicant should hold a BE/ B.Tech. or MSc with Mathematics and should have learned Programming at college level. Final year students are also eligible to apply.

Indian Institute of Technology (IIT) Hyderabad is introducing a new B. Tech. It is the first such full-fledged B. Tech. Even internationally, only select institutions like Carnegie Mellon University and Massachusetts Institute of Technology (MIT) are currently offering similar courses, claimed IIT Hyderabad in a press release. According to a report in NDTV, the B.Tech. in Artificial Intelligence course will intake 20 students each year, who will be selected through the JEE Advanced entrance examination that the students across the country take every year to get admission in the graduate courses at the IITs. Commenting on the new course, Prof U.B. Desai, Director, IIT Hyderabad, said, "The basic aim is to create a complete ecosystem for Artificial Intelligence Academics and Research at IIT Hyderabad. Moreover, the R&D will be strongly entwined with academics."

ABSTRACT: Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated. In the paper, a convolution model of forgetting curve is proposed to simulate the memory process during learning. In this model, the memory ability (i.e. the central procedure in the working memory model) and learning material (i.e. the input in the working memory model) is regarded as the system function and the input function, respectively. The status of forgetting (i.e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material. The model is applied to simulate the forgetting curves in different situations. The results show that the model is able to simulate the forgetting curves not only in one time learning condition but also in multi-times condition. The model is further verified in the experiments of Mandarin tone learning for Japanese learners. And the predicted curve fits well on the test points. Keywords: Memory forgetting curves; Convolution; Mathematical modeling 1.Introduction Since Ebbinghaus proposed the eminent'forgetting curve' to quantitatively describe the procedure of memory [1], researchers have made great effort to find out the exactly form of the models for forgetting curve. The problem is still being considered as'central theoretical importance' [2].

This webinar will provide a first-hand look at how Gradalis, a clinical-stage immunotherapy developer, is using an information management solution from L7 to streamline its research, clinical, and manufacturing operations.

This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have. Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. This course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization.

Data is the new oil. And Machine Learning is the fire. Whoever controls these two will control the world. No, the above is not some pompous phrase picked up from a dystopian novel. The new world order is all about collecting vast amounts of relevant data and processing it into actionable insights -- something the human race hasn't been able to do in history.

Learn to use Python professionally, learning both Python 3! Application or Software Development. Learn to use Object Oriented Programming Build a complete understanding of Python from the ground up! Learn to use Python professionally, learning both Python 3! You're here because you're ready to learn Programming from basics i.e. This course is designed for beginners point of view and thus does not require any prior knowledge about programming or python. At OneLit we believe in knowledge and thus this course is designed to teach students with examples. You are on the go and want to instantly learn python programming from scratch and thus this course will help you in learning python from zero by developing a calculator application.