Support vector machines are a class of techniques in data science, which had great popularity in the data science community. They are mainly used in classification tasks and perform really well when few training data is available. Sadly, SVMs have been almost forgotten lately due to the massive popularity of deep learning. But I my opinion they are a tool that every data scientist should have in their toolbox, because they are faster to train and sometimes even outperform neural networks. In this blog, you will learn that SVMs use hyperplanes to separate and classify our data.

Contemporary coding education often presents students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games. While pedagogically compelling, there are no contemporary autonomous methods for providing feedback. Notably, interactive programs are impossible to grade by traditional unit tests. In this paper we formalize the challenge of providing feedback to interactive programs as a task of classifying Markov Decision Processes (MDPs). Each student's program fully specifies an MDP where the agent needs to operate and decide, under reasonable generalization, if the dynamics and reward model of the input MDP should be categorized as correct or broken. We demonstrate that by designing a cooperative objective between an agent and an autoregressive model, we can use the agent to sample differential trajectories from the input MDP that allows a classifier to determine membership: Play to Grade. Our method enables an automatic feedback system for interactive code assignments. We release a dataset of 711,274 anonymized student submissions to a single assignment with hand-coded bug labels to support future research.

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner. In contrast to previous methods that have to scan over the entire pool and select teaching examples from it in each iteration, we propose a label synthesis teaching framework where the teacher randomly selects input teaching examples (e.g., images) and then synthesizes suitable outputs (e.g., labels) for them. We show that this framework can avoid costly example selection while still provably achieving exponential teachability. We propose multiple novel teaching algorithms in this framework. Finally, we empirically demonstrate the value of our framework.

Propositional satisfiability (SAT) is an NP-complete problem that impacts many research fields, such as planning, verification, and security. Despite the remarkable success of modern SAT solvers, scalability still remains a challenge. Main stream modern SAT solvers are based on the Conflict-Driven Clause Learning (CDCL) algorithm. Recent work aimed to enhance CDCL SAT solvers by improving its variable branching heuristics through predictions generated by Graph Neural Networks (GNNs). However, so far this approach either has not made solving more effective, or has required frequent online accesses to substantial GPU resources. Aiming to make GNN improvements practical, this paper proposes an approach called NeuroComb, which builds on two insights: (1) predictions of important variables and clauses can be combined with dynamic branching into a more effective hybrid branching strategy, and (2) it is sufficient to query the neural model only once for the predictions before the SAT solving starts. Implemented as an enhancement to the classic MiniSat solver, NeuroComb allowed it to solve 18.5% more problems on the recent SATCOMP-2020 competition problem set. NeuroComb is therefore a practical approach to improving SAT solving through modern machine learning.

Aggregated predictors are obtained by making a set of basic predictors vote according to some weights, that is, to some probability distribution. Randomized predictors are obtained by sampling in a set of basic predictors, according to some prescribed probability distribution. Thus, aggregated and randomized predictors have in common that they are not defined by a minimization problem, but by a probability distribution on the set of predictors. In statistical learning theory, there is a set of tools designed to understand the generalization ability of such procedures: PAC-Bayesian or PAC-Bayes bounds. Since the original PAC-Bayes bounds of D. McAllester, these tools have been considerably improved in many directions (we will for example describe a simplified version of the localization technique of O. Catoni that was missed by the community, and later rediscovered as "mutual information bounds"). Very recently, PAC-Bayes bounds received a considerable attention: for example there was workshop on PAC-Bayes at NIPS 2017, "(Almost) 50 Shades of Bayesian Learning: PAC-Bayesian trends and insights", organized by B. Guedj, F. Bach and P. Germain. One of the reason of this recent success is the successful application of these bounds to neural networks by G. Dziugaite and D. Roy. An elementary introduction to PAC-Bayes theory is still missing. This is an attempt to provide such an introduction.

How Greene Tweed reduced time to insights by 65% for SAP data In this latest Data Science Central Webinar, enterprises are inundated with massive amounts of SAP data and challenged to create more consumable insights without increasing headcount or system resources. It is a daunting task to deal with slow systems and manual coding to gain any value from historical data spanning thousands of products, customers, sales history, and the list goes on. In this webinar, you'll hear how global manufacturer Greene Tweed developed an efficient, cost-effective, and logical strategy to connect Qlik Data integration and Microsoft Azure Synapse to build an intelligent supply chain for better business insight. Learn how Greene Tweed addressed their challenges and how they use data liberated from SAP for strategic analytics initiatives. In addition, you will discover: • How to create a plan of attack to extract insights from massive amounts of SAP data • How important change data capture is to building low latency extracts for massive data volumes • Why automation with Qlik Data Integration is crucial to expedite data availability • How SAP data drives insights to optimize Manufacturing & Supply Chain • The gains Greene Tweed realized by moving SAP data to Azure Synapse Register today to start your journey to improving data operations, accelerating reporting, reducing systems overhead and enabling AI operations with Qlik and Azure Synapse.

Support vector machines are a class of techniques in data science, which had great popularity in the data science community. They are mainly used in classification tasks and perform really well when few training data is available. Sadly, SVMs have been almost forgotten lately due to the massive popularity of deep learning. But I my opinion they are a tool that every data scientist should have in their toolbox, because they are faster to train and sometimes even outperform neural networks. In this blog, you will learn that SVMs use hyperplanes to separate and classify our data.

The artificial intelligence market is booming, with an expected annual growth of 35.6% CAGR from 2021 to 2026. The demand for artificial intelligence experts is mounting as the economy widens and companies inflate their technology usage to improve their businesses. While this presents great opportunities for many to start a career in AI, gaining the in-demand skills can be a challenging task, especially when there is no guidance or conflicting guidance available. We have compiled a list of some of best online courses and Youtube channels which are freely available. Although various articles have published lists of top/best courses, most of them do not cater the requirement of the learners which could vary with their background.

Computer system courses (for example, computer organization, computer architecture, operating system, and compiler) are the foundation of computer science education. However, it is difficult for undergraduates to fully grasp key concepts and principles of computer systems due to the gap between theory and practice. To mitigate the gap, Chinese educators have spent the last decade focusing on teaching undergraduates to build real computer systems. They carried out many effective reform measures with the philosophy of learning-by-doing, which have significantly improved the computer system skills and abilities of Chinese undergraduates. Chinese educators have devoted exhaustive efforts over the past 10 years to reform measures for improving the technical skills of undergraduates by teaching them to build real computer systems.

Artificial intelligence (AI) has the potential to enhance every technology as it resembles enabling technologies like the combustion engine or electricity. Many people in this field believe AI is general purpose, with a multitude of applications across many different disciplines. We believe the nature of AI is interdisciplinary. In other words, the power of AI lies in augmenting its ability to accelerate research exponentially and the possibilities are endless. As a result, demand for professionals who are hard-wired in AI technology knowledge but who also possess interdisciplinary perspectives and transferable skills is becoming increasingly important.