This is the next post of a series of Trends & Perspectives blog posts. The Track Chairs will reflect on each of this years' presentation tracks, analyze and discuss some of the trends that you can expect to hear about at OLC Accelerate this year, and also get the perspectives of the Best-in-Track winners. This blog post features the presenters for the Best In Track selection for the Teaching and Learning Effectiveness Track at the upcoming OLC Accelerate Conference. Their express workshop, Show it Off! Showcase Your Artificial Intelligence, will be held on Wednesday, November 20th from 1:15-2:00pm in Southern Hemisphere 1.

Companies using data science are rapidly eating the market shares of their competitors. This shift can be observed across many industries. The root cause is that advanced analytics offers pronounced decision-making leverage. This effect is stronger in a digitized economy where a correct decision can quickly propagate through networks of businesses and consumers. Resources are rapidly allocated to the best companies.

Many programmers are moving towards data science and machine learning hoping for better pay and career opportunities -- and there is a reason for it. The Data scientist has been ranked the number one job on Glassdoor for last a couple of years and the average salary of a data scientist is over $120,000 in the United States according to Indeed. Data science is not only a rewarding career in terms of money but it also provides the opportunity for you to solve some of the world's most interesting problems. IMHO, that's the main motivation many good programmers are moving towards data science, machine learning, and artificial intelligence. If you are in the same boat and thinking about becoming a data scientist in 2019, then you have come to the right place.

Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world.

Data Science Specialization is one of the best known sets of courses offered by Coursera in conjunction with Johns Hopkins University. This specialization covers the concepts and tools you'll need throughout the entire data science pipeline. The Specialization concludes with a Capstone project that allows you to apply the skills you've learned throughout the courses. Coursera John Hopkins Data Science is a ten course program that covers the data science process from data collection to the production of data science products. It focuses on implementing the data science process in R. Coursera Johns Hopkins data science certification includes 9 courses and a capstone project.

Deep Learning Specialization provides introduction to DL methods for computer vision applications for practitioners who are familiar with the basics of DL. You will discover a breakdown and review of the convolutional neural networks course taught by Andrew Ng on deep learning specialization. It does not focus too much on math and does not include any code. After finishing the specialization you will know how to build models for photo classification, object detection, face recognition, and more. Instructors patiently explain the requisite math and programming concepts in a carefully planned order for learners who could be rusty in math/coding.

Tribune News Network Paris HE Sheikha Hind bint Hamad al Thani, vice-chairperson and CEO of Qatar Foundation, has emphasised the importance of using Artificial Intelligence as a tool for personalised learning at the 2019 Paris Peace Forum. As Artificial Intelligence (AI) and technology become increasingly powerful global tools, their role in shaping the future of education is among the topics being focused on at the annual multinational platform for dialogue, which brings together thought-leaders and decision-makers to identify solutions to the world's great challenges and places the issue of global governance at the top of the international agenda. The opening day of the forum saw HE Sheikha Hind participate in a discussion on the role of AI in education, which called for the human touch to be retained in a changing technological environment. Speakers at the session – which was held at La Grande Halle De La Villette in Paris and included Irina Bokova, former secretary-general of UNESCO, discussed the importance of guiding future generations towards becoming true digital citizens by embracing the role education must play in helping to illustrate the risks and benefits of AI. The discussion was moderated by François Taddei, director of The Center for Research and Interdisciplinarity (CRI).

Description: A down-to-earth, shy but confident take on machine learning techniques that you can put to work today.

Students at the University of Alberta are getting hands-on experience with artificial intelligence with a new robotic arm. Donated to the university's department of computing science by Kindred AI, a Canadian-based artificial intelligence company, the use of the robotic arm in the classroom helps students get a sense of reinforcement learning. Reinforcement learning is a branch of artificial intelligence, says Rapum Mahmood, assistant professor at the U of A and former Kindred AI research lead. "In reinforcement learning, we study by letting the agent interact with the environment, so that it can take the right set of actions," said Mahmood. Usually, the study is done through computer simulations and board games but in real-world applications, a robotic arm is used.

We present a reduction from reinforcement learning (RL) to no-regret online learning based on the saddle-point formulation of RL, by which "any" online algorithm with sublinear regret can generate policies with provable performance guarantees. This new perspective decouples the RL problem into two parts: regret minimization and function approximation. The first part admits a standard online-learning analysis, and the second part can be quantified independently of the learning algorithm. Therefore, the proposed reduction can be used as a tool to systematically design new RL algorithms. We demonstrate this idea by devising a simple RL algorithm based on mirror descent and the generative-model oracle. For any $\gamma$-discounted tabular RL problem, with probability at least $1-\delta$, it learns an $\epsilon$-optimal policy using at most $\tilde{O}\left(\frac{|\mathcal{S}||\mathcal{A}|\log(\frac{1}{\delta})}{(1-\gamma)^4\epsilon^2}\right)$ samples. Furthermore, this algorithm admits a direct extension to linearly parameterized function approximators for large-scale applications, with computation and sample complexities independent of $|\mathcal{S}|$,$|\mathcal{A}|$, though at the cost of potential approximation bias.