Advanced Machine Learning - Online Course
A 4-Day Livestream Seminar Taught by
Ricardo VilaltaTuesday, July 18–
Friday, July 21, 2023
10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
Machine Learning is the study of how to build computer systems that learn from experience. It is a subfield of Artificial Intelligence and intersects with statistics, cognitive science, information theory, and probability theory. Machine learning is one of the most exciting research areas today; it is becoming increasingly popular and a cornerstone in many industrial applications. For example, the computer industry is heading towards systems that can adapt and heal themselves automatically; the electronic game industry is now focusing on games where characters adapt and learn through time; and NASA is interested in robots able to adapt to any environment autonomously.
This course will address modern topics in machine learning, including neural networks, autoencoders, adversarial networks, active learning, meta-learning, clustering, and association rule mining. Attention will be given to both supervised and unsupervised learning. By the end of the course, you will have a solid understanding of how to build computer systems that learn from experience.
This course is split between lecture and “hands on” practical sessions that feature programming exercises in R. These exercises will help you learn how to pre-process, augment, and cleanse data; find patterns in data used to build predictive models; and report results using different performance metrics. You will learn how to vary model complexity to maximize performance and will be able to interpret a variety of results.
Starting July 18, we are offering this seminar as a 4-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. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
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.
The course assumes basic knowledge of R and RStudio. Each class will cover the basic R code needed to execute the machine learning concepts taught.
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. 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.
The course assumes basic knowledge of R and RStudio. Each class will cover the basic R code needed to execute the machine learning concepts taught.
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?
This course is ideal for anyone who already has good background knowledge in machine learning and is looking for additional topics to gain more expertise in the field, particularly when it comes to applications in the real world. Topics have been selected based on practical value and popularity across the machine-learning community.
The course assumes basic knowledge of introductory machine learning and a solid background in probability and statistics (linear and nonlinear regression, classification, and dimensionality reduction).
The following book will be helpful as a reference in case some concepts are new or challenging (available online by the authors): “An Introduction to Statistical Learning with Applications in R” by G. James, D. Witten, T. Hastie, and R. Tibshirani. Springer, 2013.
This course is ideal for anyone who already has good background knowledge in machine learning and is looking for additional topics to gain more expertise in the field, particularly when it comes to applications in the real world. Topics have been selected based on practical value and popularity across the machine-learning community.
The course assumes basic knowledge of introductory machine learning and a solid background in probability and statistics (linear and nonlinear regression, classification, and dimensionality reduction).
The following book will be helpful as a reference in case some concepts are new or challenging (available online by the authors): “An Introduction to Statistical Learning with Applications in R” by G. James, D. Witten, T. Hastie, and R. Tibshirani. Springer, 2013.
Seminar outline
Day 1
-
- Introduction to advanced machine learning
- Neural networks
- Perceptron, backpropagation, recurrent networks
Day 2
-
- Deep learning
- Deep neural Nnetworks, deep autoencoders
- Adversarial networks
Day 3
-
- Active learning
- Improving the efficiency of acquiring class labels
- Meta learning
- Automatic model selection, hyper-parameter tuning
Day 4
-
- Unsupervised learning
- PCAs, association rule mining
Day 1
-
- Introduction to advanced machine learning
- Neural networks
- Perceptron, backpropagation, recurrent networks
Day 2
-
- Deep learning
- Deep neural Nnetworks, deep autoencoders
- Adversarial networks
Day 3
-
- Active learning
- Improving the efficiency of acquiring class labels
- Meta learning
- Automatic model selection, hyper-parameter tuning
Day 4
-
- Unsupervised learning
- PCAs, association rule mining
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.