In this module, we introduce the core idea of teaching a computer to learn concepts using data--without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.

Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA community. We argue that such boundaries are artificial and perhaps unnecessary, given the reasoning abilities we seek to teach are not governed by the format. As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats. UnifiedQA performs on par with 9 different models that were trained on individual datasets themselves. Even when faced with 12 unseen datasets of observed formats, UnifiedQA performs surprisingly well, showing strong generalization from its out-of-format training data. Finally, simply fine-tuning this pre-trained QA model into specialized models results in a new state of the art on 6 datasets, establishing UnifiedQA as a strong starting point for building QA systems.

In the framework of Granular Computing (GC), Interval type 2 Fuzzy Sets (IT2 FSs) play a prominent role by facilitating a better representation of uncertain linguistic information. Perceptual Computing (Per C), a well known computing with words (CWW) approach, and its various applications have nicely exploited this advantage. This paper reports a novel Per C based approach for student strategy evaluation. Examinations are generally oriented to test the subject knowledge of students. The number of questions that they are able to solve accurately judges success rates of students in the examinations. However, we feel that not only the solutions of questions, but also the strategy adopted for finding those solutions are equally important. More marks should be awarded to a student, who solves a question with a better strategy compared to a student, whose strategy is relatively not that good. Furthermore, the students strategy can be taken as a measure of his or her learning outcome as perceived by a faculty member. This can help to identify students, whose learning outcomes are not good, and, thus, can be provided with any relevant help, for improvement. The main contribution of this paper is to illustrate the use of CWW for student strategy evaluation and present a comparison of the recommendations generated by different CWW approaches. CWW provides us with two major advantages. First, it generates a numeric score for the overall evaluation of strategy adopted by a student in the examination. This enables comparison and ranking of the students based on their performances. Second, a linguistic evaluation describing the student strategy is also obtained from the system. Both these numeric score and linguistic recommendation are together used to assess the quality of a students strategy. We found that Per-C generates unique recommendations in all cases and outperforms other CWW approaches.

During this pandemic, many organizations are offering free or drastically cheaper courses to help people skill-up for when we eventually get out of lock-down. There are numerous outlets if you want to learn to code from, for instance, Freecodecamp or the Free Fridays scheme from General Assembly. And for gamers, Gamedev.tv has taken 80% off its courses, where you can learn to code by building video games. However, most online coding courses, either free or paid, essentially suggest you download a project or copy-paste code from their snippets going through their courses. They tend not to include Integrated Development Environments, which are more helpful in the learning process.

Udemy Coupon Code Link: Make predictions with Python machine learning for apps Udemy Make predictions with Python machine learning for apps. With the help of this course you can Leverage TensorFlow models to build & improve apps! What you'll learn Master the basics: become an expert in Python and Java while learning core machine learning concepts Machine learning goes mobile: learn how to incorporate machine learning models into Android apps Optimize for intelligent apps: discover the TensorFlow mobile framework and build scientific analysis apps Description Go through 3 ultimate levels of artificial intelligence for beginners! This course was funded by a wildly successful Kickstarter Use Google's deep learning framework TensorFlow with Python. Leverage machine learning to improve your apps Prediction Models Masterclass By the end of this course you will have 3 complete mobile machine learning models and apps.

The University of Liverpool has won a share of £13m from the Office for Students (OfS) to fund 36 scholarships on two innovative new postgraduate conversion courses. Through specialist teaching and paid work placements, the conversion courses will allow graduates from both STEM (science, technology, engineering and mathematics) and non or far-STEM subjects to significantly boost their digital skills – helping to address the shortage of Artificial Intelligence (AI) and data specialists joining the UK workforce. The scholarships are targeted at graduates from backgrounds often underrepresented in these industries, particularly female, disabled and black students. The two programmes offered by the University of Liverpool will accept their first intake of students from September this year. There is a 12-month full-time Data Science and Artificial Intelligence MSc course and a 24-month full-time option which includes a paid'year in industry' placement.

I have been a data analytics professional for the past twelve years. Throughout my career, I have seen a steady spike in the use of data across the industry, be it engineering, education, healthcare or financial services. It was in 2017 when I read about the Economist article "The world's most valuable resource is no longer oil, but data" an idea which was first coined by Clive Humby, UK Mathematician and architect of Tesco's Clubcard in 2006. Many prominent personalities like Meglena Kuneva, European Consumer Commissioner, 2009 [1] later reiterated this. I could see everyone talking about the infinite potential of data and how to use it in a million ways.

Many measurements and searches for new phenomena performed by the experiments at the Large Hadron Collider (LHC) use a classification algorithm, such as Boosted Decision Trees or Neural Networks, to discriminate the physics process of interest (signal) from other physics processes with similar signature (background). The algorithms are optimized using supervised training on detailed simulated Monte Carlo (MC) data sets, labeled as signal or background. The resulting classifier is applied to unlabeled data to separate signal and background, and measure the statistical significance of the signal or its strength, assuming that the simulated and the real data sets are identically distributed. However, differences between real and simulated data sets always exist and the learner may pick up a discriminating feature which differs between the data sets, introducing a bias to the sample used for training. This problem is similar to that of visual recognition where training is performed on simulated pictures, the so-called source domain and applied to real photographs, the target domain. In order to avoid training specific to the source domain, algorithms of domain adaptation have been developed. In this paper, we apply the method of domain adaptation to high energy physics data. In this paper we present a Domain Adversarial Neural Network (DANN) to classify events in the search for the t tH(H b b) process at the LHC, which is very rare and hard to separate from the t t jets background [1].

Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users. A semantic parser should be introspective of its uncertainties and prompt for user demonstration when uncertain. In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions. To combat the sparsity of demonstration, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and re-trains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011). We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem.

Existing question answering datasets mostly contain homogeneous contexts, based on either textual or visual information alone. On the other hand, digitalization has evolved the nature of reading which often includes integrating information across multiple heterogeneous sources. To bridge the gap between two, we compile a Diverse Visuo-Lingustic Question Answering (DVLQA) challenge corpus, where the task is to derive joint inference about the given image-text modality in a question answering setting. Each dataset item consists of an image and a reading passage, where questions are designed to combine both visual and textual information, i.e. ignoring either of them would make the question unanswerable. We first explore the combination of best existing deep learning architectures for visual question answering and machine comprehension to solve DVLQA subsets and show that they are unable to reason well on the joint task. We then develop a modular method which demonstrates slightly better baseline performance and offers more transparency for interpretation of intermediate outputs. However, this is still far behind the human performance, therefore we believe DVLQA will be a challenging benchmark for question answering involving reasoning over visuo-linguistic context. The dataset, code and public leaderboard will be made available at https://github.com/shailaja183/DVLQA.