Hello guys, you may know that Machine Learning and Artificial Intelligence have become more and more important in this increasingly digital world. They are now providing a competitive edge to businesses like NetFlix's Movie recommendations. If you have just started in this field and looking for what to learn then I am going to share 5 essential Machine learning algorithms you can learn as a beginner. These essential algorithms form the basis of most common Machine learning projects and having a good knowledge of them will not only help you to understand the project and model quickly but also to change them as per your need. Machine learning by a simple word is the science or the field of making the computer learn like a human by feeding it with the data and without being programmed and it separate into two categories the first one is classification problems which the machine needs to classify between two objects or more like between human and animal and the second is regression problems which the machine need to produce an output based on a previous data.

The platform has the ability of facial recognition for the learners with 90% accuracy which is especially helpful in cheating prevention. Moreover, the platform detects student focus and engagement and alerts the instructors in case of inactivity.

Hi Everyone welcome to new course which is created to sharpen your linear regression and statistical basics. In this course I have explained hypothesis testing, Unbiased estimators, Statistical test, Gradient descent. End of the course you will be able to code your own regression algorithm from scratch.Who this course is for: Hi my name is Jay working as data scientist in a leading MNC, I have completed my masters degree adv mathematics and FEM . I love making educational video and content.

Intern at our partner companies for 12-16 weeks and gain first hand industry experience. Most of the companies hire our interns for Entry level and Experienced roles before closure of internships. Take part of in-house research on AI, IoT, VLSI and ML for 12-16 weeks and solve industry challenges.... Interested students opt for full time research in-house or co-innovation at a collaborating university. Most of our students started at DesignNation, are now pursuing PhD and Post-doctoral studies at USA, Canada and Australia. More than 70% jobs, now are upgraded to new technologies. Get ready for change and avail better pay by upgrading... yourself to new technologies.

The role of artificial intelligence in education is changing and AI's impact on student learning will be big. Learn more about How AI Can Help in Changing the Education System?

What is the vision and mission strategy of MBZUAI? The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) was established as the world's first graduate-level, research-based artificial intelligence (AI) University, with the aim to empower students, businesses and governments to advance AI as a global force for positive progress. It complements the ambitions of Abu Dhabi, and the UAE as a whole, to use the transformative potential of AI to support economic diversification. Our intention through MBZUAI is to evolve interdisciplinary, collaborative research, and development capability in the field of AI – and of course, educating students to become future leaders and innovators is a significant component of this. Students at MBZUAI will be provided with the depth and breadth of knowledge that will enable them to use AI as a tool to foster knowledge creation and economic growth.

Artificial Intelligence will change lives. It will change the economy. It will change the world. You hear about it on the news and you see Google and other tech companies come out with this ridiculously advanced products which make you think "Oh god, in 20 years I'll have a terminator roaming around in my neighborhood". Now, while I can't say that that won't happen I can at least say that this is not at all the case with today's technology.

This dissertation investigates integer linear programming (ILP) formulation of Bayesian Network structure learning problem. We review the definition and key properties of Bayesian network and explain score metrics used to measure how well certain Bayesian network structure fits the dataset. We outline the integer linear programming formulation based on the decomposability of score metrics. In order to ensure acyclicity of the structure, we add ``cluster constraints'' developed specifically for Bayesian network, in addition to cycle constraints applicable to directed acyclic graphs in general. Since there would be exponential number of these constraints if we specify them fully, we explain the methods to add them as cutting planes without declaring them all in the initial model. Also, we develop a heuristic algorithm that finds a feasible solution based on the idea of sink node on directed acyclic graphs. We implemented the ILP formulation and cutting planes as a \textsf{Python} package, and present the results of experiments with different settings on reference datasets.

Standard RL algorithms assume fixed environment dynamics and require a significant amount of interaction to adapt to new environments. We introduce Policy-Dynamics Value Functions (PD-VF), a novel approach for rapidly adapting to dynamics different from those previously seen in training. PD-VF explicitly estimates the cumulative reward in a space of policies and environments. An ensemble of conventional RL policies is used to gather experience on training environments, from which embeddings of both policies and environments can be learned. Then, a value function conditioned on both embeddings is trained. At test time, a few actions are sufficient to infer the environment embedding, enabling a policy to be selected by maximizing the learned value function (which requires no additional environment interaction). We show that our method can rapidly adapt to new dynamics on a set of MuJoCo domains. Code available at https://github.com/rraileanu/policy-dynamics-value-functions.

This paper describes our submission to subtask a and b of SemEval-2020 Task 4. For subtask a, we use a ALBERT based model with improved input form to pick out the common sense statement from two statement candidates. For subtask b, we use a multiple choice model enhanced by hint sentence mechanism to select the reason from given options about why a statement is against common sense. Besides, we propose a novel transfer learning strategy between subtasks which help improve the performance. The accuracy scores of our system are 95.6 / 94.9 on official test set and rank 7$^{th}$ / 2$^{nd}$ on Post-Evaluation leaderboard.