Many scientific fields -- including biology, health, education, and the social sciences -- use machine learning (ML) to help them analyze data at an unprecedented scale. However, ML researchers who develop advanced methods rarely provide detailed tutorials showing how to apply these methods. Existing tutorials are often costly to participants, presume extensive programming knowledge, and are not tailored to specific application fields. In an attempt to democratize ML methods, we organized a year-long, free, online tutorial series targeted at teaching advanced natural language processing (NLP) methods to computational social science (CSS) scholars. Two organizers worked with fifteen subject matter experts to develop one-hour presentations with hands-on Python code for a range of ML methods and use cases, from data pre-processing to analyzing temporal variation of language change. Although live participation was more limited than expected, a comparison of pre- and post-tutorial surveys showed an increase in participants' perceived knowledge of almost one point on a 7-point Likert scale. Furthermore, participants asked thoughtful questions during tutorials and engaged readily with tutorial content afterwards, as demonstrated by 10K~total views of posted tutorial recordings. In this report, we summarize our organizational efforts and distill five principles for democratizing ML+X tutorials. We hope future organizers improve upon these principles and continue to lower barriers to developing ML skills for researchers of all fields.

A human resources department that manages a range of duties from strategic planning, employee welfare, and preserving employee branding is crucial for practically all firms throughout the world. The HR department is always working on a variety of projects that have to do with developing hiring strategies, employee training, payroll, employee welfare, and other things. However, it is challenging for HR professionals to keep up with the pace and manage all the tasks with the growing employee strength and strong attention to keeping the company's identity. In this situation, technology has become the HR department's saviour. There are excellent opportunities to considerably reduce the HR effort given the current need for AI and automation for recruiting and employee engagement activities.

Intro UK is a very popular option among candidates who seek to study ML. This is because British universities provide stupendous machine-learning training to students. This is the key reason why many ML students enroll themselves in the master in a machine learning program at British universities. But which British educational institution should you join to complete your studies? So, to answer this question today we are sharing with you the top four universities in the UK to study MS in ML.

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library.

In this work, we consider the task of improving the accuracy of dynamic models for model predictive control (MPC) in an online setting. Although prediction models can be learned and applied to model-based controllers, these models are often learned offline. In this offline setting, training data is first collected and a prediction model is learned through an elaborated training procedure. However, since the model is learned offline, it does not adapt to disturbances or model errors observed during deployment. To improve the adaptiveness of the model and the controller, we propose an online dynamics learning framework that continually improves the accuracy of the dynamic model during deployment. We adopt knowledge-based neural ordinary differential equations (KNODE) as the dynamic models, and use techniques inspired by transfer learning to continually improve the model accuracy. We demonstrate the efficacy of our framework with a quadrotor, and verify the framework in both simulations and physical experiments. Results show that our approach can account for disturbances that are possibly time-varying, while maintaining good trajectory tracking performance.

General mathematical reasoning is computationally undecidable, but humans routinely solve new problems. Moreover, discoveries developed over centuries are taught to subsequent generations quickly. What structure enables this, and how might that inform automated mathematical reasoning? We posit that central to both puzzles is the structure of procedural abstractions underlying mathematics. We explore this idea in a case study on 5 sections of beginning algebra on the Khan Academy platform. To define a computational foundation, we introduce Peano, a theorem-proving environment where the set of valid actions at any point is finite. We use Peano to formalize introductory algebra problems and axioms, obtaining well-defined search problems. We observe existing reinforcement learning methods for symbolic reasoning to be insufficient to solve harder problems. Adding the ability to induce reusable abstractions ("tactics") from its own solutions allows an agent to make steady progress, solving all problems. Furthermore, these abstractions induce an order to the problems, seen at random during training. The recovered order has significant agreement with the expert-designed Khan Academy curriculum, and second-generation agents trained on the recovered curriculum learn significantly faster. These results illustrate the synergistic role of abstractions and curricula in the cultural transmission of mathematics.

This is a follow-up tutorial article of our previous article entitled "Robot Basics: Representation, Rotation and Velocity". For better understanding of the topics covered in this articles, we recommend the readers to first read our previous tutorial article on robot basics. Specifically, in this article, we will cover some more advanced topics on robot kinematics, including robot motion, forward kinematics, inverse kinematics, and robot dynamics. For the topics, terminologies and notations introduced in the previous article, we will use them directly without re-introducing them again in this article. Also similar to the previous article, math and formulas will also be heavily used in this article as well (hope the readers are well prepared for the upcoming math bomb). After reading this article, readers should be able to have a deeper understanding about how robot motion, kinematics and dynamics. As to some more advanced topics about robot control, we will introduce them in the following tutorial articles for readers instead.

The course was updated on November 4th, 2022, entirely. The new content is a significant improvement to the old course, with a better course structure, more real-world apps, and using the latest version of Python and other recent programming tools. The course assumes you have never programmed before and teaches Python from zero. This is the only course that follows a multimodal learning approach that offers students both a video course and an environment that simulates real-world programming activities similar to a real bootcamp. Students learn Python by building programs from scratch, adding new features to existing programs, improving existing features, fixing bugs, engaging in code experiments, learning programming tools that every programmer should know, deploying apps in the cloud, and engaging with other fellow students.

One of the biggest threats to productivity in recent times is context switching. It is a term originating from computer science but applied to humans it refers to the process of stopping work on one thing, performing a different task, and then picking back up the initial task. During a work day, you might want to check something on Stack Overflow, for example, which normalization technique to choose for your project. While doing so, you start exploring the documentation of scikit-learn to see which approaches are already implemented and how they compare against each other. This might lead to you some interesting comparison articles on Medium or video tutorials on YouTube.

Artificial Intelligence (AI) is much in the news these days. As a concept, AI seems completely unrelated to the field of law. Although, AI and Law are intricately intertwined and are becoming more so each day. In this lecture, Professor Harry Surden – a former software engineer and leader of the emerging interdisciplinary field of AI and Law – will explore: What is Artificial Intelligence? How is law affecting Artificial Intelligence?