Advanced Machine Learning with R - Online Course
An 8-Hour Livestream Seminar Taught by
Ross JacobucciWednesday, May 1 –
Thursday, May 2, 2024
10:30am-12:30pm (convert to your local time)
1:00pm-3:00pm
This is one of two seminars offered by Professor Jacobucci on advanced machine learning methods. While this seminar aims to provide you with the core knowledge needed to apply and evaluate advanced algorithms, the other seminar, Applied Deep Learning using Python, focuses on applying deep learning algorithms to text and image data, along with insight into how deep learning methodologies can shape the design of studies. Register for one or both.
Machine learning (i.e., artificial intelligence, big data, supervised learning, data science) has enormously impacted academic research and industry. Machine learning algorithms have transformed the analysis of extensive datasets, empowering us to uncover deeper insights and make more informed decisions by leveraging advanced data processing and pattern recognition capabilities.
While machine learning has become more widely available and easier to use, it presents several challenges, including preventing overfitting, interpreting the results it produces, and dealing with the inevitable issues that arise from analyzing diverse data types. At its core, most machine learning applications involve applying complex algorithms to predict a single outcome, with more advanced applications often only requiring slight modifications. This course aims to provide each participant with the core knowledge needed to apply and evaluate advanced algorithms.
Starting May 1, we are offering this seminar as an 8-hour synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two 2-hour lecture sessions which include hands-on exercises, separated by a 30-minute break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
The course focuses on traditional tabular data (i.e., row = people; columns = variables). It assumes participants are familiar with regularization in regression, cross-validation, and decision trees. Course content includes predicting single outcomes (supervised learning) and tree-based ensemble methods (random forests; boosting). Concepts will be paired with hands-on exercises showing how to apply, assess, and interpret these methods.
The course will closely follow Chapters 3-8 of the author’s co-authored book Machine Learning for Social and Behavioral Research (Jacobucci, Grimm, & Zhang, 2023). This book is not required, but can serve as a reference for those wishing additional information.
The course focuses on traditional tabular data (i.e., row = people; columns = variables). It assumes participants are familiar with regularization in regression, cross-validation, and decision trees. Course content includes predicting single outcomes (supervised learning) and tree-based ensemble methods (random forests; boosting). Concepts will be paired with hands-on exercises showing how to apply, assess, and interpret these methods.
The course will closely follow Chapters 3-8 of the author’s co-authored book Machine Learning for Social and Behavioral Research (Jacobucci, Grimm, & Zhang, 2023). This book is not required, but can serve as a reference for those wishing additional information.
Computing
This seminar will use R for the empirical examples and exercises. You are strongly encouraged to use a computer with R and RStudio installed to participate in the hands-on exercises. RStudio Desktop is a freely available interface for R.
This seminar presumes at least some exposure to the R computing environment, namely reading in and manipulating data frames and applying regression models.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
This seminar will use R for the empirical examples and exercises. You are strongly encouraged to use a computer with R and RStudio installed to participate in the hands-on exercises. RStudio Desktop is a freely available interface for R.
This seminar presumes at least some exposure to the R computing environment, namely reading in and manipulating data frames and applying regression models.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
If you have an introductory knowledge of machine learning and want to learn the more advanced concepts, this course is for you. The material in this course builds off of the topics taught in Machine Learning, requiring at least familiarity with logistic regression, decision trees, and regularized regression, along with the concepts of cross-validation and bootstrapping.
If you have an introductory knowledge of machine learning and want to learn the more advanced concepts, this course is for you. The material in this course builds off of the topics taught in Machine Learning, requiring at least familiarity with logistic regression, decision trees, and regularized regression, along with the concepts of cross-validation and bootstrapping.
Seminar outline
Day 1: Advanced Prediction
-
- Concepts
- Advanced regularization
- Gradient boosting
- Random forests
- SuperLearner
- Intro to neural networks
- Applications and exercises
Day 2: Assessing Prediction
-
- Concepts
- Interpretation
- Regression & classification fit metrics
- Imbalanced data
- Advanced cross-validation
- Parallel and high-performance computing
- Applications and exercises
- Calculating variable importance
- Visualizing relationships
- Implementing nested k-fold cross-validation
- Calculating probability and class-based metrics
Day 1: Advanced Prediction
-
- Concepts
- Advanced regularization
- Gradient boosting
- Random forests
- SuperLearner
- Intro to neural networks
- Applications and exercises
- Concepts
Day 2: Assessing Prediction
-
- Concepts
- Interpretation
- Regression & classification fit metrics
- Imbalanced data
- Advanced cross-validation
- Parallel and high-performance computing
- Applications and exercises
- Calculating variable importance
- Visualizing relationships
- Implementing nested k-fold cross-validation
- Calculating probability and class-based metrics
- Concepts
Payment information
The fee of $695 includes all course materials.
**Email info@statisticalhorizons.com to receive a $200 total discount when you register for both of Professor Jacobucci’s advanced machine learning courses.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $695 includes all course materials.
**Email info@statisticalhorizons.com to receive a $200 total discount when you register for both of Professor Jacobucci’s advanced machine learning courses.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.