Group discussions are essential for organizing every aspect of modern life, from faculty meetings to senate debates, from grant review panels to papal conclaves. While costly in terms of time and organization effort, group discussions are commonly seen as a way of reaching better decisions compared to solutions that do not require coordination between the individuals (e.g. voting)---through discussion, the sum becomes greater than the parts. However, this assumption is not irrefutable: anecdotal evidence of wasteful discussions abounds, and in our own experiments we find that over 30% of discussions are unproductive. We propose a framework for analyzing conversational dynamics in order to determine whether a given task-oriented discussion is worth having or not. We exploit conversational patterns reflecting the flow of ideas and the balance between the participants, as well as their linguistic choices. We apply this framework to conversations naturally occurring in an online collaborative world exploration game developed and deployed to support this research. Using this setting, we show that linguistic cues and conversational patterns extracted from the first 20 seconds of a team discussion are predictive of whether it will be a wasteful or a productive one.

First of all let me tell you what is Open CV and what are the things that we can do using OpenCV. OpenCV is a open source C library for digital image processing and computer vision, which can be used to create real time face recognisation and using it with embedded robotics and micro controllers for purpose like differentiating a specific color from an image having various colors. Solution to all this we will cover in this course. "Few years back, I started learning programming and spent couple of months just to learn the basics. Then, for again a couple of months I spent my time learning advance of Open CV. Being in the same field for almost one year, I decided to start my own project. But I keep on stuck at various steps of my project as many of concepts were not cleared. I was not able to develop a simple software from the knowledge I gained. I was depressed and thinking to leave the programming. Then one day, I decided to give it one more try. I wrote down all the parts of my programming knowledge where I had weak concepts. I started visiting forums and posting my questions to sharpen my skills and doubt clearance. And again tried to create that project with fewer difficulties. I repeated the same method again and dig a lot. Now I got success, I am a professional programmer in C and OpenCV and now working with two companies."

Artificial Intelligence seems to be everywhere at the moment. Massive tech companies are spending vast amounts of money on research and development of ever more sophisticated AI-focused tools, services and other assorted products. News outlets give a huge amount of coverage to events in which AI is the star of the show. Perhaps most importantly, AI is being discussed more by the public at large. Yet for the vast majority of people, AI is a nebulous area.

This is a compilation has everything you need to jumpstart your skills in the core tasks of data transformation, modeling, and visualization. MODELING Below is a list of popular analysis from Rexer's 2013 survey. The table is biased towards customer transaction, text, and social media data. CRAN has pages dedicated to each typical task of statistical computing http://cran.r-project.org/web/views/ Python has several packages tailored for statistical analysis including Pandas, Orange, PyBrain and Scikit-learn TRANSFORMATION OpenRefine is designed to help journalists and other non technical people organize incomplete data from different sources.

Unless you've recently graduated from one of the new Data Science courses that have been popping up online and in various universities around the world, then becoming a Data Scientist was most likely slightly accidental and was more about the journey than the destination. I started out as a physicist and had a strong mathematical grounding, but I had a passion for medicine. After completing my bachelor's degree I took a master's degree in medical physics. This is where I gained an appreciation for the importance of image analysis and the role that data plays in medicine. I created a virtual model of a human torso by segmenting images from the Visible Human Project.

Artificial intelligence (AI) is already in the classroom: as digital textbooks that include question-and- answer simulations; as intelligent nano-tutors to help students work through complex problems and as intelligent systems to grade student assignments. Ashok Goel teaches Knowledge-Based AI as part of the Institute's Online Master of Science in Computer Science (OMS CS) program. He says he and his peers are on the verge of ushering AI into higher education in bold, new ways. What's next are virtual teaching assistants (VTAs). This modern form of AI will become omnipresent and available on demand for students.

This paper addresses an open challenge in educational data mining, i.e., the problem of automatically mapping online courses from different providers (universities, MOOCs, etc.) onto a universal space of concepts, and predicting latent prerequisite dependencies (directed links) among both concepts and courses. We propose a novel approach for inference within and across course-level and concept-level directed graphs. In the training phase, our system projects partially observed course-level prerequisite links onto directed concept-level links; in the testing phase, the induced concept-level links are used to infer the unknown course-level prerequisite links. Whereas courses may be specific to one institution, concepts are shared across different providers. The bi-directional mappings enable our system to perform interlingua-style transfer learning, e.g. treating the concept graph as the interlingua and transferring the prerequisite relations across universities via the interlingua. Experiments on our newly collected datasets of courses from MIT, Caltech, Princeton and CMU show promising results.

This paper considers the problem of matrix completion when some number of the columns are completely and arbitrarily corrupted, potentially by a malicious adversary. It is well-known that standard algorithms for matrix completion can return arbitrarily poor results, if even a single column is corrupted. One direct application comes from robust collaborative filtering. Here, some number of users are so-called manipulators who try to skew the predictions of the algorithm by calibrating their inputs to the system. In this paper, we develop an efficient algorithm for this problem based on a combination of a trimming procedure and a convex program that minimizes the nuclear norm and the $\ell_{1,2}$ norm. Our theoretical results show that given a vanishing fraction of observed entries, it is nevertheless possible to complete the underlying matrix even when the number of corrupted columns grows. Significantly, our results hold without any assumptions on the locations or values of the observed entries of the manipulated columns. Moreover, we show by an information-theoretic argument that our guarantees are nearly optimal in terms of the fraction of sampled entries on the authentic columns, the fraction of corrupted columns, and the rank of the underlying matrix. Our results therefore sharply characterize the tradeoffs between sample, robustness and rank in matrix completion.

Even though Gilles has recently graduated with a degree in Fundamental Mathematics, he knows that there's more to be done than mathematics. With a solid knowledge in classical statistics, he now pursues a PhD in parallelizing regression modeling techniques. Vincent has just finished his Master's degree in Artificial Intelligence, and has more than 3 years of experience with machine learning problems of different kinds. He experienced first-hand the difficulties that come with building and assessing machine learning systems. This made him passionate about teaching people how to do machine learning the right way.

A former science teacher who believed in the power and possibility of online learning over two decades ago, he taught himself how to build courses in HTML on class intranets. Kevin taught one of the first hybrid, educational technology courses for teachers, for the University of Washington. And, after building countless web pages and classes on the early world wide web, he now helps develop e-learning programs, consults on virtual training'best practices' and has many interests in other internet and educational technology-related areas. Kevin finds he's now enjoying learning more from his children who are all deep into their own technology-related careers and entrepreneurial endeavors. With two new grandchildren, he's investigating more seriously the advancing new technologies in an effort to understand the knowledge and skills necessary to achieve happiness and success in a technological future.