Machine Learning - Online Course
A 4-Day Livestream Seminar Taught by
Seth FlaxmanMachine learning has emerged as a major field at the intersection of statistics and computer science where the goal is to create reliable and flexible predictive models. This seminar offers a thorough introduction to supervised machine learning methods. Topics covered include: supervised learning; loss functions and optimization; cross-validation; the bias/variance tradeoff; high-dimensional variable selection and regularization methods; non-linear methods such as random forests and support vector machines; an introduction to deep learning; and an overview of the R programming language.
*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.
This course is designed for those who have no previous experience with machine learning. If you are looking to learn more advanced methods, check out Advanced Machine Learning.
More details about the course content
Machine Learning methods have gained much attention for their applicability to large datasets: large in terms of the number of observations and/or the number of variables. While a vast selection of learning methods and models are available, almost all can be framed in terms of finding parameters to minimize a loss function. A small set of general principles ensures good performance.
This course will introduce machine learning, R, and RStudio. It will also focus on model selection, variable selection, and regularization with the goal of building robust models with good generalization performance. The seminar will focus on non-linear methods, including support vector machines, random forest, and deep learning. Throughout the course, you will gain experience with these methods through hands-on exercises.
Machine Learning methods have gained much attention for their applicability to large datasets: large in terms of the number of observations and/or the number of variables. While a vast selection of learning methods and models are available, almost all can be framed in terms of finding parameters to minimize a loss function. A small set of general principles ensures good performance.
This course will introduce machine learning, R, and RStudio. It will also focus on model selection, variable selection, and regularization with the goal of building robust models with good generalization performance. The seminar will focus on non-linear methods, including support vector machines, random forest, and deep learning. Throughout the course, you will gain experience with these methods through hands-on exercises.
Computing
This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R and RStudio installed. RStudio is a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
Basic familiarity with R and RStudio is highly desirable, but novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R and RStudio installed. RStudio is a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
Basic familiarity with R and RStudio is highly desirable, but novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
If you have a desire to learn the fundamental principles of Machine Learning, to see how it can help you explore your data and build accurate predictive models, this course is for you. You should have a knowledge of linear or logistic regression.
If you have a desire to learn the fundamental principles of Machine Learning, to see how it can help you explore your data and build accurate predictive models, this course is for you. You should have a knowledge of linear or logistic regression.
Seminar outline
Day 1: Introduction to machine learning
- Introduction to machine learning: supervised vs. unsupervised learning
- Introduction to R and RStudio
- Linear models (including linear regression and logistic regression)
- Loss functions and optimization
Day 2: Model selection
- Cross-validation
- The bias-variance tradeoff
- Lasso and ridge regression: regularization and variable selection methods
Day 3: Non-linear methods & deep learning
- Decision trees
- Random forests
- Support vector machines
Day 4: Deep learning
- Stochastic gradient descent
- Neural networks
- Deep learning
Day 1: Introduction to machine learning
- Introduction to machine learning: supervised vs. unsupervised learning
- Introduction to R and RStudio
- Linear models (including linear regression and logistic regression)
- Loss functions and optimization
Day 2: Model selection
- Cross-validation
- The bias-variance tradeoff
- Lasso and ridge regression: regularization and variable selection methods
Day 3: Non-linear methods & deep learning
- Decision trees
- Random forests
- Support vector machines
Day 4: Deep learning
- Stochastic gradient descent
- Neural networks
- Deep learning
Payment information
The fee of $895 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $895 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.