Description This course provides a brief introduction to the fundamentals of the R language and focuses on its use for data analysis–including exploratory data analysis, linear and logistic regression, variable selection, model diagnostics, and prediction. Syllabus Fundamentals of R & RStudio: the basics–including objects, subsetting, indexing, data I/O, and control structures Exploratory Data Analysis: all the necessary tools to investigate your data before performing any formal modeling–from summary statistics to visualization including plotting histograms, boxplots, and scatterplots Linear Regression: everything…

This course provides an introduction to NNs and learning with Python. In this workshop, you will learn about Tensorflow programming fundamentals and basic ideas of neural networks through building and training different MNIST models. Moreover, you will be introduced to more advanced applications of deep learning in computer vision and natural language processing with Keras high-level API. Date: May 31, 2018 Time: 9 a.m. to 5 p.m. with lunch provided Location: Donald Bren Hall, Room 4011 Instructor:  Lingge Li, Julian Collado…

This workshop aims to introduce both users of scripting languages and advanced programmers to the Julia ecosystem and explore details about the Julia language which can help produce efficient and readable code. The goal of the workshop is for students to understand where Julia can be applied and be well-equipped to start using Julia in their own research. Students will learn about the current state of Julia development (IDEs, documentation, where to get help), how to write efficient code by…

This course builds on our Introduction to R by teaching advanced visualization and data tidying. In this course, you will cover advanced visualization including ggplot and R Shiny. In addition to visualizations, principles of tidy data and reporting will be discussed. Date: May 4, 2018 Time:9 a.m. to 5 p.m. with lunch provided Location: Donald Bren Hall, Room 4011 Instructor:  Dustin Pluta, UC Irvine Pre-requisites: 1) familiarity with basic statistical concepts, and 2) intermediate R programming knowledge. For the tutorial, bring a laptop…

This course provides an introduction to fundamentals of data analysis using the R language. In this course, you will learn how to effectively use R for data analysis. This course provides a brief introduction to the fundamentals of the R language and focuses on its use for data analysis–including exploratory data analysis, linear and logistic regression, variable selection, model diagnostics, and prediction. Practical examples will be provided during the course. Date: April 20, 2018 Time: 9 a.m. to 5 p.m.…

Philosophy of Machine Learning: Knowledge and Causality The recent rapid development in machine learning has opened up unprecedented possibilities in analyzing and predicting diverse phenomena. From the humanities, to the social and cognitive sciences, to the natural sciences, fields previously closed off to predictive analysis are now the subject of machine learning investigations. The purpose is to introduce workshop participants to a diverse collection of perspectives and methodologies in the hope of engendering further interdisciplinary thinking. Date:  March 17-18, 2018 (Saturday-Sunday)…

This course provides an introduction to fundamentals of data analysis using the R language. In this course, you will learn how to effectively use R for data analysis. This course provides a brief introduction to the fundamentals of the R language and focuses on its use for data analysis–including exploratory data analysis, linear and logistic regression, variable selection, model diagnostics, and prediction. Practical examples will be provided during the course. Date: March 2, 2018 Time: 9 a.m. to 5 p.m.…