Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads. However, ASAG is a very challenging task due to two reasons: (1) student answers are made up of free text which requires a deep semantic understanding; and (2) the questions are usually open-ended and across many domains in K-12 scenarios. In this paper, we propose a generalized end-to-end ASAG learning framework which aims to (1) autonomously extract linguistic information from both student and reference answers; and (2) accurately model the semantic relations between free-text student and reference answers in open-ended domain. The proposed ASAG model is evaluated on a large real-world K-12 dataset and can outperform the state-of-the-art baselines in terms of various evaluation metrics. 1 Introduction Assessing the knowledge acquired by students is one of the most important aspects of the learning process as it provides feedback to help students correct their misunderstanding of knowledge and improves their overall learning performance. Traditionally, the assessing paradigm is often conducted by instructors or teachers. However, this access paradigm is not suitable in many cases especially when teaching resources are not readily available.

Top Stories, Tweets Top Stories, Aug 5-11: Knowing Your Neighbours: Machine Learning on Graphs; What is Benford's Law and why is it important for data science? Top KDnuggets tweets, Jul 31 - Aug 06: NLP vs. NLU: from Understanding a Language to Its Processing News Exploratory Data Analysis Using Python Meetings The slow, startling triumph of Reverend Bayes - John Elder's 2019 Keynote at PAW in London Cambridge Analytica whistleblower Chris Wylie to headline Big Data LDN 2019 keynote programme Academic Postdoctoral position (2 years) in multivariate analysis and deep learning PhD student position in computational science with focus on chemistry Monash University: Research Fellow - Computer Vision [Melbourne, Australia] Image of the week 12 NLP Researchers, Practitioners, Innovators to Follow Learn how to do Machine Learning on Graphs; Follow these 12 amazing leaders in NLP; Read the explanation of Deep Learning for NLP, including ANNs, RNNs and LSTMs; Understand what is Benford's Law and why is it important for data science; Find the 6 key concepts in Andrew NG Machine Learning Yearning; and more. Knowing Your Neighbours: Machine Learning on Graphs 12 NLP Researchers, Practitioners & Innovators You Should Be Following Deep Learning for NLP: ANNs, RNNs and LSTMs explained! What is Benford's Law and why is it important for data science?

The Directorate General of Training (DGT), under the skill development and entrepreneurship ministry, has signed an agreement with IT major IBM to carry out a nationwide Train-the-Trainer programme in basic artificial intelligence, an official statement said on Wednesday. As part of the programme, ITI trainers will be trained on basic artificial intelligence (AI) skills towards using the technology in their day-to-day training activities, the ministry said in a statement. This programme, it said, aims at enabling the trainers with basic approach, workflow and application of artificial intelligence that they can apply in their training modules. "IBM aims at training 10,000 faculty members from ITIs across the country and the programme will be executed over a period of one year with 14 trainers across 7 locations with over 200 workshops," it added. Mahendra Nath Pandey, Minister for Skill Development and Entrepreneurship said, many more training programmes will be initiated for the trainers.

It's a mystery that deep learning works so well. Even though there are several hints about why deep neural networks are so effective, the truth is that nobody is entirely sure and theoretical understanding of deep learning is very much an active area of research. We will make neural networks paint abstract images for us, and then we will interpret those images to develop a better intuition on what might be happening under the hood. Also, as a bonus, by the end of the tutorial, you'll be able to generate images such as the following (everything is less than 100 lines of PyTorch code. This image was generated by a simple architecture called Compositional Pattern Producing Networks (CPPN) which I got introduced to via this blog post. In that blog post, the author generates abstract images via neural networks written in JavaScript.

Intuition lectures on topics ranging from Classical CV techniques like HOG, SIFT to Convolutional Neural Network based techniques like Overfeat, Faster RCNN etc. You will learn how the ideas have evolved from some of the earliest papers to current ones. Intuition lectures on topics ranging from Classical CV techniques like HOG, SIFT to Convolutional Neural Network based techniques like Overfeat, Faster RCNN etc. You will learn how the ideas hav... more

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. In previous years, we have brought collections of predictions and analysis from experts. What were the main developments in Machine Learning and Artificial Intelligence in 2018, and what key trends do you expect in 2019? Below are the responses from Anima Anandkumar, Andriy Burkov, Pedro Domingos, Ajit Jaokar, Nikita Johnson, Zachary Chase Lipton, Matthew Mayo, Brandon Rohrer, Elena Sharova, Rachel Thomas, and Daniel Tunkelang. Key themes singled out by these experts include deep learning advancements, transfer learning, the limitations of machine learning, the changing landscape of natural language processing, and much more. Be sure to check out collected opinions we shared last week when we asked a group of experts the related question, "What were the main developments in Data Science and Analytics in 2018 and what key trends do you expect in 2019?" Anima Anandkumar (@AnimaAnandkumar) is Director of ML research at NVIDIA and Bren Professor at Caltech.

Data visualization: In this section, you will learn how to create simple plots like scatter plot histogram bar, etc. Data manipulation: You will learn in detail about data manipulation. GUI Programming: This section is a combination of life instructor-led training and self-paced learning. Developing web Maps and representing information using plots: In this section, you will understand how to design Python applications. Computer vision using open CV and visualization using bokeh: You will also learn designing Python application in the section.

When we create our machine learning models, a common task that falls on us is how to tune them. People end up taking different manual approaches. Some of them work, and some don't, and a lot of time is spent in anticipation and running the code again and again. So that brings us to the quintessential question: Can we automate this process? A while back, I was working on an in-class competition from the "How to win a data science competition" Coursera course.

Each day, we read more news about artificial intelligence (AI), machine learning (ML) and their uses for not only work but, more importantly, education. About a year ago, I started to research these areas. While I understood the concepts of both and could offer a decent definition, I was not able to easily identify what it might look like in today's classrooms. My first interaction with machine learning came some years ago when I worked on my Spanish translation coursework. Our focus was on the level of accuracy that ML-translation provided for students and for businesses looking to use these services.

This is a 1-week/10 hours long, part-time and instructor-led training offered in evening time (New York Timezone) by 6FS.io, a San Francisco based technology company. This training program is built based on 6FS team's years of experience in building large-scale solutions using various various Big Data and AI/ML technologies. This is not a book-based training, rather a hands-on, interactive experience app building apps using AI/ML, delivered by experienced startup CTOs. While learning basic concepts like Python, Jupyter notebooks, and training models and human powered labeling, you'll also learn practical problems and solutions, based on how Dean and Adrian built technology stacks in their previous startups. Let's build a project to gather data from human labeling service like AWS Sage maker GroundTruth.