Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done.

Road traffic noise outside schools may impair the development of a child's attention span and short-term memory. Previous studies have shown that noise pollution from road traffic can disrupt sleep and increase stress in adults. Meanwhile, local aircraft noise has been shown to reduce academic performance and reading comprehension in children. However, it wasn't known whether road traffic noise outside schools impacts cognitive development in children. To learn more, Maria Foraster at the Barcelona Institute for Global Health and her colleagues recruited 2680 children aged 7 to 10 from 38 schools throughout Barcelona.

Sen. Ron Wyden, D-Ore., speaks during a Senate Finance Committee hearing on Oct. 19, 2021. Wyden says he has long been concerned about the algorithms used by his state's child welfare system. Sen. Ron Wyden, D-Ore., speaks during a Senate Finance Committee hearing on Oct. 19, 2021. Wyden says he has long been concerned about the algorithms used by his state's child welfare system. Child welfare officials in Oregon will stop using an algorithm to help decide which families are investigated by social workers, opting instead for a new process that officials say will make better, more racially equitable decisions.

Data is information collected through observations. It is often a set of qualitative and quantitative variables or a compilation of both. Data often entered in a system can have multiple layers of issues while retrieving, which in most cases will cause you to clean the data before you can make sense of the same and process the same to come up with actionable insights. Data cleaning is a very crucial first step in any machine learning project. It is an inevitable step in the process of model building and data analysis, but no one really can or tells you how to go about the same.

Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it? You may show him/her a dog and say "here is a dog" and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognise different breeds of dogs which he hasn't even seen. Similarly, in Supervised Learning, we have two sets of variables.

Do you have any favorite internal affirmations or mantras you like to offer students to help them feel confident in the face of potential bullies or put-downs? I'm trying to help out my elementary schooler. Right now I'm thinking of sayings along the lines of "I am awesome just as I am," or "I will not let others define me." Additionally, if there are any great all-purpose verbal comebacks or responses that might be applied generally, those would be helpful and welcome as well. Really, I welcome any suggestions you have for helping my child muster up confidence (with kindness). I speak to my students about this all the time.

The first annual Day of AI on Friday, May 13 introduced artificial intelligence literacy to classrooms all over the world. An initiative of MIT Responsible AI for Social Empowerment and Education (RAISE), Day of AI is an opportunity for teachers to introduce K-12 students of all backgrounds to artificial intelligence (AI) and its role in their lives. With over 3,000 registrations from educators across 88 countries -- far exceeding the first-year goal of 1,000 registrations in the United States -- the initiative has clearly struck a chord with students and teachers who want to better understand the technology that's increasingly part of everyday life. In today's technology-driven world, kids are exposed to and interact with AI in ways they might not realize -- from search algorithms to smart devices, video recommendations to facial recognition. Day of AI aims to help educators and students develop AI literacy with an easy entry point, with free curricula and hands-on activities developed by MIT RAISE for grades 3-12.

G-Protein Coupled Receptors (GPCRs) are a big family of eukaryotic cell transmembrane proteins, responsible for numerous biological processes. From a practical viewpoint around 34\% of the drugs approved by the US Food and Drug Administration target these receptors. They can be analyzed from their simulated molecular dynamics, including the prediction of their behavior in the presence of drugs. In this paper, the capability of Long Short-Term Memory Networks (LSTMs) are evaluated to learn and predict the molecular dynamic trajectories of a receptor. Several models were trained with the 3D position of the amino acids of the receptor considering different transformations on the position of the amino acid, such as their centers of mass, the geometric centers and the position of the $\alpha$--carbon for each amino acid. The error of the prediction of the position was evaluated by the mean average error (MAE) and root-mean-square deviation (RMSD). The LSTM models show a robust performance, with results comparable to the state-of-the-art in non-dynamic 3D predictions. The best MAE and RMSD values were found for the mass center of the amino acids with 0.078 {\AA} and 0.156 {\AA} respectively. This work shows the potential of LSTM to predict the molecular dynamics of GPRCs.

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science.