SC6: An ML/AI Tutorial: From Basics to Advanced

MONDAY, JUNE 2 | 8:00 – 10:45 AM

ABOUT THIS COURSE: This tutorial will simplify key AI concepts to help you connect the dots when you need to understand an AI project. The course will also help you appreciate the hype and hope areas of AI, which should further enable you to make the right strategic decisions for drug development at your organization. This course will be targeted towards teams in Biomedicine. We will cover:

Instructor:

Bino John, PhD, Associate Director, Data Science, Clinical Pharmacology & Safety Sciences, Data Science and AI, AstraZeneca

WHAT YOU WILL LEARN:

  • ML/AI key introductory concepts
  • Regression
  • ML Classification methods: KNN, Decision trees/Random Forest, Naïve Bayes, Markov/HMM, SVM, NeuralNets/DeepLearning
  • Tips on succeeding with ML

INSTRUCTOR BIOGRAPHY:

John_BinoBino John, PhD, Associate Director, Data Science, Clinical Pharmacology & Safety Sciences, Data Science and AI, AstraZeneca

Bino John, PhD, currently leads a variety of Artificial Intelligence (AI) Initiatives at AstraZeneca (AZ). As an Associate Director at AZ, he is leveraging Deep learning and other advanced AI approaches to accelerate Drug development. Before joining AZ in 2018, Bino led a variety of computational biology initiatives and teams at Dow and then Dow-DuPont. In those roles, his efforts included enabling machine learning/AI and integrative big-data informatics capabilities for genomics research for the Agricultural Sector. He earned an Integrated Master’s degree in Chemistry from the Indian Institute of Technology (Mumbai) in 2000 and subsequently received his PhD from The Rockefeller University in Biomedical Sciences in 2003. His thesis research in computational structural biology with Dr. Andrej Sali was followed by postdoctoral studies in computational genomics with Dr. Chris Sander at the Memorial Sloan-Kettering Cancer Centre. In 2005, Bino joined the University of Pittsburgh as a faculty, where he focused on using high-throughput methods for cancer biomarker discovery, resulting in the discovery of novel molecules and molecular pathways.