Advanced Machine Learning - Online Course
A 3-Day Livestream Seminar Taught by
Seth Flaxman10:00am-12:30pm (convert to your local time) Thursday-Saturday
1:30pm-4:00pm Thursday, 1:30pm-3:30pm Friday & Saturday
Machine 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 assumes a basic familiarity with machine learning and covers statistical machine learning, Bayesian machine learning, kernel methods and Gaussian processes, Bayesian probabilistic programming with MCMC and variational inference, Bayesian Additive Regression Trees, deep generative modeling with variational autoencoders, convolution neural networks, and recurrent neural networks.
Starting October 13, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour 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.
More details about the course content
Machine learning methods have gained much attention for their applicability to large and complex datasets: large in terms of the number of observations and/or the number of variables, complex in terms of structure: inputs and/or outputs which are vector-valued, text, images, and more. While a vast array of off-the-shelf learning methods and models are available for simple classification and regression tasks, more complex problems require a deeper understanding of the strengths, weaknesses, and principles underlying various approaches, and how they can fit together to solve real-world problems.
The first day introduces statistical machine learning, Bayesian machine learning, and probabilistic programming. The second day focuses on Gaussian processes, kernel methods, and neural networks. The third day goes into depth on deep learning, covering variational autoencoders, convolution neural networks, and recurrent neural networks. 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 and complex datasets: large in terms of the number of observations and/or the number of variables, complex in terms of structure: inputs and/or outputs which are vector-valued, text, images, and more. While a vast array of off-the-shelf learning methods and models are available for simple classification and regression tasks, more complex problems require a deeper understanding of the strengths, weaknesses, and principles underlying various approaches, and how they can fit together to solve real-world problems.
The first day introduces statistical machine learning, Bayesian machine learning, and probabilistic programming. The second day focuses on Gaussian processes, kernel methods, and neural networks. The third day goes into depth on deep learning, covering variational autoencoders, convolution neural networks, and recurrent neural networks. Throughout the course, you will gain experience with these methods through hands-on exercises.
Computing
This seminar will use R for 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, Linux and cloud 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 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, Linux and cloud 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 go beyond basic machine learning, to see how it can help you explore your data and build useful and advanced models, this course is for you. You should have experience with supervised machine learning, and basic familiarity with the R programming language.
If you have a desire to go beyond basic machine learning, to see how it can help you explore your data and build useful and advanced models, this course is for you. You should have experience with supervised machine learning, and basic familiarity with the R programming language.
Seminar outline
Day 1: R, Statistical and Bayesian Machine Learning
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- Brief refresher on R and RStudio
- Statistical machine learning
- Bayesian machine learning
- MCMC and variational inference
- Bayesian probabilistic programming
Day 2: From Kernel Methods to Neural Networks
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- Gaussian processes and kernel methods
- Multilayer perceptrons
- Loss functions and learning curves
- Stochastic gradient descent
Day 3: Deep Learning In-depth
-
- Pre-trained networks and fine-tuning
- Variational autoencoders
- Convolution neural networks
- Recurrent neural networks
Day 1: R, Statistical and Bayesian Machine Learning
-
- Brief refresher on R and RStudio
- Statistical machine learning
- Bayesian machine learning
- MCMC and variational inference
- Bayesian probabilistic programming
Day 2: From Kernel Methods to Neural Networks
-
- Gaussian processes and kernel methods
- Multilayer perceptrons
- Loss functions and learning curves
- Stochastic gradient descent
Day 3: Deep Learning In-depth
-
- Pre-trained networks and fine-tuning
- Variational autoencoders
- Convolution neural networks
- Recurrent neural networks
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.