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This week Peter will present ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation by Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Julius Busecke · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Tian Zheng · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Mike Pritchard.

We look forward to seeing you there!

Want to know more Paper Club?

• We discuss a different research paper every week. We post each week's paper in our GitHub repo - please read it before the meetup.
• All events will be hosted on a Google Meets video call. Once a month we also host an in-person event in our London office - watch this space for updates.
• All recorded presentations can be found in our YouTube channel (don't forget to subscribe!).

This book guides readers through the essentials of applied statistics and machine learning using the R programming language. By delving into robust data processing techniques, visualization, and statistical modeling with R, you will develop skills to effectively analyze data and design predictive models. Each chapter includes hands-on exercises to reinforce the concepts in a practical, intuitive way. What this Book will help me do Understand and apply key statistical concepts such as probability distributions and hypothesis testing to analyze data. Master foundational mathematical principles like linear algebra and calculus relevant to data science and machine learning. Develop proficiency in data manipulation and visualization using robust R libraries such as dplyr and ggplot2. Build predictive models through practical exercises and learn advanced concepts like Bayesian statistics and linear regression. Gain the practical knowledge needed to apply statistical and machine learning methodologies in real-world scenarios. Author(s) Liu Peng is an accomplished author with a strong academic and practical background in statistics and data science. Armed with extensive experience in applying R to real-world problems, he brings a blend of technical mastery and teaching expertise. His commitment is to transform complex concepts into accessible, enriching learning experiences for readers. Who is it for? This book is ideal for data scientists and analysts ranging from beginners to those at an intermediate level. It caters especially to those interested in practicing statistical modeling and learning R in depth. If you have basic familiarity with statistics and are looking to expand your data science capabilities using R, this book is well-suited for you.