The COVID-19 pandemic has revealed the importance of digital fabrication to enable online learning, which remains a challenge for robotics courses. We introduce a teaching methodology that allows students to participate remotely in a hands-on robotics course involving the design and fabrication of robots. Our methodology employs 3D printing techniques with flexible filaments to create innovative soft robots; robots are made from flexible, as opposed to rigid, materials. Students design flexible robotic components such as actuators, sensors, and controllers using CAD software, upload their designs to a remote 3D printing station, monitor the print with a web camera, and inspect the components with lab staff before being mailed for testing and assembly. At the end of the course, students will have iterated through several designs and created fluidically-driven soft robots. Our remote teaching methodology enables educators to utilize 3D printing resources to teach soft robotics and cultivate creativity among students to design novel and innovative robots. Our methodology seeks to democratize robotics engineering by decoupling hands-on learning experiences from expensive equipment in the learning environment.

This repository contains instructions, template source code and examples on how to serve/deploy machine learning models using various frameworks and applications such as Docker, Flask, FastAPI, BentoML, Streamlit, MLflow and even code on how to deploy your machine learning model as an android app. The Repository also has code and how-to's for deploying your apps to various cloud platforms(AWS, Heroku, Vercel etc), working with Github actions for CI/CD(Continuous Integration and Continuous Development), TDD(Test driven development) with pytest and other useful information. Before we get into building and deploying our models we'll have to setup our environment. I use'pyenv' for managing different versions of python and pyenv-virtualenv for setting up virtual environments. You can learn how to install pyenv on your operating system by checking out their official github.

For better or worse, it seems as though the term "artificial intelligence" (AI) is becoming synonymous with modern machine learning. Whereas AI used to encompass many types of computational techniques used in the simulation of intelligence in machines, it almost exclusively refers to the state of the art (SOTA) advances in one branch of modern machine learning in particular: deep learning. Deep neural networks had already been around for some time when Krizhevsky, Sutskever and Hinton's ImageNet victory in October 2012 kicked off the modern deep learning revolution. Since then, deep learning has gone on to conquer SOTA results in almost every AI subfield and domain it has been put up against: computer vision; natural language processing; speech recognition; medical image analysis; image reconstruction; text generation; and much more. Simply put, if you want to be involved in modern AI development, you need to understand deep learning.

We present MDEAW, a multimodal database consisting of Electrodermal Activity (EDA) and Photoplethysmography (PPG) signals recorded during the exams for the course taught by the teacher at Eurecat Academy, Sabadell, Barcelona in order to elicit the emotional reactions to the students in a classroom scenario. Signals from 10 students were recorded along with the students' self-assessment of their affective state after each stimulus, in terms of 6 basic emotion states. All the signals were captured using portable, wearable, wireless, low-cost, and off-the-shelf equipment that has the potential to allow the use of affective computing methods in everyday applications. A baseline for student-wise affect recognition using EDA and PPG-based features, as well as their fusion, was established through ReMECS, Fed-ReMECS, and Fed-ReMECS-U. These results indicate the prospects of using low-cost devices for affective state recognition applications. The proposed database will be made publicly available in order to allow researchers to achieve a more thorough evaluation of the suitability of these capturing devices for emotion state recognition applications.

Decision trees are a popular intuitive supervised machine learning algorithm, that is part of the sklearn library, and has wide areas of applications like- business growth opportunities evaluation, demographic-driven data client targeting, and strategic management planning. Every machine learner worth their salt needs to familiarize themselves with the decision trees machine learning model. These free machine learning with random forests and decision trees pdf course notes will teach you how do decision trees work, how they ensemble into the random forest algorithm, what are their pros and cons, which are the most commonly used performance metrics and much more.

In this tutorial we are going to deep dive into image classification may be deep learning practitioners may not know how exactly the model is working. The above concepts will be revealed step by step. The above cell of code basically unzips the file from the link of pet and saves directories path to variable path. The main difference between localization and classification is: In classification, we get to know what is the object instead of where localization addresses. The above file which is returned is not list-type but a collection object of the class called L which is the advanced version of Python List with added common operations. Let us have a look - ( great_pyrenees_173.jpg).

Having just wrapped up a successful ODSC Europe, we're now turning our attention to ODSC West 2022 and we couldn't be more excited to announce our first group of speakers. These innovators and experts have helped shape the fields of data science and AI into what we have today, and will continue to do so in the years to come. You can find a full list of our currently confirmed ODSC West speakers here, and a sneak peek of just a few of them (and their session topics) below. In recent years, the fields of NLP, robotics, and computer vision, among others, have seen significant advancement thanks to Self-supervised and Unsupervised learning techniques. This session will provide hands-on examples of how you can apply large language models and transformers to zero-shot and few-shot learning in NLP applications.

According to a report by New Media Consortium, adaptive learning (AL) and learning analytics are two crucial developments emerging in the educational technology market. Today, students pay more and more attention to individualized learning and instruction. If you are one of the higher ed institutions ramping up efforts to improve learning outcomes, implementing adaptive learning systems can be the potential solution. In this article at Hackernoon, Shannon Flynn explains how big data shapes AL. AL is an online educational system that focuses on understanding the student.

Abstract knowledge is deeply grounded in many computer-based applications. An important research area of Artificial Intelligence (AI) deals with the automatic derivation of knowledge from data. Machine learning offers the according algorithms. One area of research focuses on the development of biologically inspired learning algorithms. The respective machine learning methods are based on neurological concepts so that they can systematically derive knowledge from data and store it. One type of machine learning algorithms that can be categorized as "deep learning" model is referred to as Deep Neural Networks (DNNs). DNNs consist of multiple artificial neurons arranged in layers that are trained by using the backpropagation algorithm. These deep learning methods exhibit amazing capabilities for inferring and storing complex knowledge from high-dimensional data. However, DNNs are affected by a problem that prevents new knowledge from being added to an existing base. The ability to continuously accumulate knowledge is an important factor that contributed to evolution and is therefore a prerequisite for the development of strong AIs. The so-called "catastrophic forgetting" (CF) effect causes DNNs to immediately loose already derived knowledge after a few training iterations on a new data distribution. Only an energetically expensive retraining with the joint data distribution of past and new data enables the abstraction of the entire new set of knowledge. In order to counteract the effect, various techniques have been and are still being developed with the goal to mitigate or even solve the CF problem. These published CF avoidance studies usually imply the effectiveness of their approaches for various continual learning tasks. This dissertation is set in the context of continual machine learning with deep learning methods. The first part deals with the development of an ...

We study meta-learning for adversarial multi-armed bandits. We consider the online-within-online setup, in which a player (learner) encounters a sequence of multi-armed bandit episodes. The player's performance is measured as regret against the best arm in each episode, according to the losses generated by an adversary. The difficulty of the problem depends on the empirical distribution of the per-episode best arm chosen by the adversary. We present an algorithm that can leverage the non-uniformity in this empirical distribution, and derive problem-dependent regret bounds. This solution comprises an inner learner that plays each episode separately, and an outer learner that updates the hyper-parameters of the inner algorithm between the episodes. In the case where the best arm distribution is far from uniform, it improves upon the best bound that can be achieved by any online algorithm executed on each episode individually without meta-learning.