Knowledge tracing (KT) refers to a machine learning technique to assess a student's level of understanding (so-called knowledge state) of a certain concept based on the student performance on problem solving. KT accepts a series of question-answer pairs as an input and iteratively updates the knowledge state of the student, eventually returning the probability of the student solving an unseen question. From the viewpoint of neuroeducation (the field of applying neuroscience, cognitive science, and psychology to education), however, KT leaves much room for improvement in terms of explaining the complex process of human learning. In this paper, we identify three problems of KT (namely non adaptive knowledge growth, neglected latent information, and unintended negative influence) and propose a memory-network-based technique named intelligent knowledge tracing (IKT) to address them, thus approaching one step closer to understanding the complex mechanism underlying human learning. In addition, we propose a new performance metric called correct update count (CUC) that can measure the degree of unintended negative influence, thus quantifying how closely a student model resembles the human learning process. The proposed CUC metric can complement the area under the curve (AUC) metric, allowing us to evaluate competing models more effectively. According to our experiments using a widely used public benchmark, IKT significantly (over two times) outperformed the existing KT approaches in terms of CUC, while preserving the correctness behavior measured in AUC.

Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100.

Structure: Good structure and went through all the basic principles of statistics in detail. Appreciated how it did not have to go through the methodology of each method, but taught us how to appreciate it and understand the data as it was presented in the literature. I liked how John went through the examples in the literature so it was good to see how it was utilised in practice. I wish there was a separate course to teach us how to use these methods with sample data, perhaps a taster of this would have been good to include? but I do understand that would be challenging for some. I think some in-video questions would have been good to check-up on the progress of learning.

For some us, AI is kind of an iffy proposition. To many, it is nebulous enough to seem like it might replace us or our jobs. And the harbingers of this sea change aren't exactly affirming: every other week in the news, self-driving smart cars keep crashing, with injuries and sometimes fatalities. AI generally doesn't seem to be that well-received in mass media, either, like in movies like Minority Report or TV shows like Westworld. Because of all this, the public perception of AI might be on the negative side. A good way to overcome uneasiness, anxiety or fear is simply be learning more about whatever seems to be the issue or problem.

I must have read hundreds of articles preaching strategies to achieve success. They tell me pick up these habits, follow these simple steps, adopt these rules to live by etc. etc. etc. They all insist I get up approximately 4 hours earlier than I want to and eat exclusively plant based substitutes that cost 3 times the price of the original product. The question I have is, if I were to follow the steps and achieve success what would that mean? How can I tell if I am successful?

Artificial Intelligence is one of the hottest field in computer science at the moment and has taken the world by storm as a major field of development and research. Python has emerged as a dominant language in AI/ML programming because of its simplicity and flexibility. Are you a Python developer who is interested to build real-world Artificial Intelligence applications? If so, A-Z Artificial Intelligence with Python is for you! This comprehensive 5-in-1 training course is designed such that you can add an intelligence layer to any application that's based on images, text, stock market, or some other form of data.

Artificial Intelligence is a broader umbrella under which Machine Learning (ML) and Deep Learning (DL) comes. Diagram shows, ML is subset of AI and DL is subset of ML. AI is composed of 2 words Artificial and intelligence. Anything which is not natural and created by humans is artificial. Intelligence means ability to understand, reason, plan etc.

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About this course: This course will teach you how to create useful chatbots without the need to write any code. Leveraging IBM Watson's Natural Language Processing capabilities, you'll learn how to plan, implement, test, and deploy chatbots that delight your users, rather than frustrate them. True to our promise of not requiring any code, you'll learn how to visually create chatbots with Watson Assistant (formerly Watson Conversation) and how to deploy them on your own website through a handy WordPress plugin. No worries, one will be provided to you. Chatbots are a hot topic in our industry and are about to go big.

This is the May edition of the AI/ML learning resources newsletter -- I compiled a list learning resources, most of which are recently announced or upcoming in the near future. A few of these may have been around for a while but I recently discovered them. We are living in very exciting times when so many AI/ML learning resources are being launched at such a rapid pace. Many companies and individuals are working hard to bring AI/ML and deep learning to everyone, and I'm joining that effort by sharing. Google I/O 2018 (5/8–5/10) has tons of sessions on AI/ML.