Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand. We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research, as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such systems and help to hold them accountable. New transparency approaches are developed at breakneck speed, enabling us to peek inside these black boxes and interpret their decisions. Many of these techniques are introduced as monolithic tools, giving the impression of one-size-fits-all and end-to-end algorithms with limited customisability. Nevertheless, such approaches are often composed of multiple interchangeable modules that need to be tuned to the problem at hand to produce meaningful explanations. This paper introduces a collection of hands-on training materials -- slides, video recordings and Jupyter Notebooks -- that provide guidance through the process of building and evaluating bespoke modular surrogate explainers for tabular data. These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation.

If you ever generated an AI face with (DALL-E, Midjourney, Stable Diffusion) you will often notice that the eyes in the image are not symmetrical and look weird. Now you can use the perfect tool for fixing that problem, it is called: CodeFormer. CodeFormer can help with Face Restoration, Face Color Enhancement and Restoration, and Face Inpainting.

This course, run by IBM, is a great introduction to using Python for ML. It is incredibly useful for grasping how to leverage both supervised and unsupervised learning algorithms, giving you all the knowledge you need to start applying both methods in your day-to-day work. If you want to expand your Python knowledge for data science applications, look no further than this course.

Note: By registering for Day 1 you will automatically be registered for Day 2. You cannot register for Day 2. This page is a placeholder. This workshop teaches you techniques for training deep neural networks on multi-GPU technology to shorten the training time required for data-intensive applications. Working with deep learning tools, frameworks, and workflows to perform neural network training, you'll learn concepts for implementing PyTorch multi-GPUs to reduce the complexity of writing efficient distributed software and to maintain accuracy when training a model across many GPUs. Workshop format: Interactive presentation with hands-on exercises Target audience: This workshop is intended for researchers that would like to use multiple GPUs to train deep learning models in PyTorch. Knowledge prerequisites: Participants should be comfortable with training deep learning models using a single GPU.

HELM Learning (Helping Everyone Learn More) is the first online peer-to-peer learning platform which allows students (typically middle-to-high school students) to teach classes and students (typically elementary-to-middle school students) to learn from classes for free. This method of class structure (peer-to-peer learning) has been proven effective for learning, as it promotes teamwork and collaboration, and enables active learning. HELM is a unique platform as it provides an easy process for students to create, teach and learn topics in a structured, peer-to-peer environment. Since HELM was created in April 2020, it has gotten over 4000 student sign ups and 80 teachers, in 4 continents around the world. HELM has grown from a simple website-and-Google-Form platform to having a backend system coded with Python, SQL, JavaScript and HTML, hosted on an AWS service. This not only makes it easier for students to sign up (as the students' information is saved in an SQL database, meaning they can sign up for classes without having to put in their information again, as well as getting automated emails about their classes), but also makes it easier for teachers to teach (as supplemental processes such as creating Zoom links, class recording folders, sending emails to students, etc. are done automatically). In addition, HELM has a recommendation machine learning algorithm which suggests classes and subjects students would enjoy taking, based on the previous classes a student has taken. This has created an easier experience for students to sign up for classes they are interested in.

Amidst the COVID-19 pandemic, the authors of this paper organized a Reinforcement Learning (RL) course for a graduate school in the field of data science. We describe the strategy and materials for creating an exciting learning experience despite the ubiquitous Zoom fatigue and evaluate the course qualitatively. The key organizational features are a focus on a competitive hands-on setting in teams, supported by a minimum of lectures providing the essential background on RL. The practical part of the course revolved around Hearts Gym, an RL environment for the card game Hearts that we developed as an entry-level tutorial to RL. Participants were tasked with training agents to explore reward shaping and other RL hyperparameters. For a final evaluation, the agents of the participants competed against each other.

Learning a new language can be confusing and challenging. You are bombarded with YouTube videos claiming to teach you Python in 10 minutes. In the end, you get more confused and give up. Find out ways that work for you." Even if you understand the basics, you will get more confused in selecting and learning new tools. Furthermore, without structured learning, you will fail to pass any coding interview or test. Just like any skill, you need persistence and practice. In this blog, I have converted my Python learning experience into 7 easy to follow tips. Let's start the journey of becoming an expert Python programmer. Learning everything about Python is not necessary, but you need to build a base. For that, you need to understand the basics. There are plenty more things to learn, but for the starter stick to basics and practice. It is ok to make mistakes, forget the syntax, and get stuck in simple things. Do not force yourself to memorize. The most important thing is that you learn ...

This is another Free Coursera course to learn how deep learning with neural networks can be used to classify images and detect objects in images and videos. In this course, you will use convolutional neural networks (CNNs) to classify images and detect objects.

In this work, we propose a meta algorithm that can solve a multivariate global optimization problem using univariate global optimizers. Although the univariate global optimization does not receive much attention compared to the multivariate case, which is more emphasized in academia and industry; we show that it is still relevant and can be directly used to solve problems of multivariate optimization. We also provide the corresponding regret bounds in terms of the time horizon $T$ and the average regret of the univariate optimizer, when it is robust against nonnegative noises with robust regret guarantees.