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Livestream Seminar

Advanced Machine Learning

A 3-Day Livestream Seminar Taught by

Ross Jacobucci
Course Dates: Ask about upcoming dates
Schedule: All sessions are held live via Zoom. All times are ET (New York time).

10:00am-2:00pm ET (New York time): Live lecture via Zoom
4:00pm-5:00pm ET: Live lab session via Zoom (Thursday and Friday only)

Machine learning–including artificial intelligence, big data, supervised learning, and data science–has had an enormous impact in both academic research and industry. Development of innovative machine learning algorithms has been paired with the availability of large datasets and has facilitated the collection of even larger datasets, often times containing novel data types (e.g., text).

While machine learning has become increasingly easy to apply in many programming languages, it also presents a number of challenges; specifically, how to interpret the relationships between variables, how to prevent overfitting, and how to deal with the inevitable issues that arise from collecting diverse data types.

This seminar builds off of introductory materials on machine learning, assuming a basic familiarity with the ideas behind regularization in regression, cross-validation, and decision trees. The first day covers the state-of-the-art algorithms for prediction problems with a single outcome. The second day focuses on putting everything together, namely, how to best run all of these algorithms and properly compare their results. Finally, the third day discusses a host of algorithms that were each developed for different types of unsupervised learning tasks. Understanding how each algorithm works will be paired with material on how to apply the method with minimal coding in R.

Starting May 5, we are offering this seminar as a 3-day synchronous*, livestream workshop. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. 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.

Each day will include a hands-on exercise to be completed on your own after the lecture session is over. An additional lab session will be held Thursday and Friday afternoons, where you can review the exercise results with the instructor and ask any questions.

*We understand that scheduling is difficult during this unpredictable time. 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.

Computing

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