Learn more about the practice of predictive modeling using Python. Description: Learn more about the practice of predictive modeling using Python. To find out about future one-day courses, please join our mailing list. This course builds upon Predictive Modeling with Python, covering best practices for building predictive models that perform well in noisy, real-world domains. […]
This course introduces Natural Language Processing to users familiar with Python. This course will introduce fundamental concepts in NLP including word and document representation, text processing, document classification, document similarity, and clustering, and dimensionality reduction. Date: March 3, 2017 Time:9 a.m. to 12:30 p.m. Location: Donald Bren Hall, Room 4011 Instructor: Garren Gaut, UC Irvine Pre-requisites: 1) A […]
Machine Learning and Human Behavior Symposium The last few years have seen an avalanche of new data sources providing scientists with a unique opportunity to study human behavior. In fact, much of the data now labeled as “Big Data” is generated by us, humans, as a result of our interactions with other people and […]
This course provides an introduction to the fundamentals of the R language and its applications to data analysis. In this course, you will learn how to program in R and how to effectively use R for data analysis. The course covers an introduction to data/object types in R, reading data, creating data visualizations, accessing and […]
Learn about the use of predictive models in Python through scikit-learn. Python is a popular language for scientific processing and machine learning. This course will introduce general modeling concepts in addition to concrete examples based on the scikit-learn library. Example usage of scikit-learn will illustrate how to fit and evaluate predictive models. Regression and classification […]
Data collected in time and/or space exhibit unique properties that require attention to draw proper conclusions from statistical analyses. In this workshop, students are introduced to statistical concepts that are particularly useful for analyzing spatial-temporal data. Using R and Python, students will learn the basic mathematics of spatial-temporal analysis via hands-on exercises, and will put […]
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 12, 2017 Time:9 a.m. to 5 p.m. with lunch provided Location: Donald Bren […]
This course is for researchers who have never used Linux and/or a computer cluster and introduces concepts and best practices for both. Description: This course covers how to best exploit the bash shell for both interactive work and batch jobs, moving & simple manipulation of data, as well very short introductions to programming in bash, […]
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 […]
Learn about the use of predictive models in Python through scikit-learn. Python is a popular language for scientific processing and machine learning. This course will introduce general modeling concepts in addition to concrete examples based on the scikit-learn library. Example usage of scikit-learn will illustrate how to fit and evaluate predictive models. Regression and classification […]