Machine Learning Toronto
This two day training course will provide delegates with an in-depth understanding of machine learning applications. This course will be a technical look at machine learning and provide suggestions and strategies for integrating it within your organization
This two day training course will provide delegates with an in-depth understanding of machine learning applications. This course will be a technical look at machine learning and provide suggestions and strategies for integrating it within your organization.
The multi-tutor format will provide attendees with an understanding of key theory, models, and more advanced tools in machine learning solutions through a quantitative approach that will also consider portfolio construction, trading, risk management and other business areas.
- Unique multi speaker format featuring sessions from key industry practitioners and academics
- Get insight into the big data revolution and the building blocks of machine learning tools in finance
- Understanding machine learning methodology from a quantitative viewpoint
- Learn the theory behind machine learning, deep learning and neural networks, and how these methods can be applied in your organization
- Gain insight into the latest and most widely used industry applications
- Get a clear view of ML/AI Capabilities in finance, how they can help you solve problems more effectively and drive your business forward
Who Should Attend
This course is primarily aimed at those working in financial institutions; as well as regulatory bodies, advisory firms and technology vendors. However Risk Training welcomes anyone who would benefit from this training. Specific job titles may include:
- Machine Learning
- Portfolio Management
- Asset Allocation
- Data Science
- Financial Engineering
- Quantitative Analytics
- Quantitative Modelling
- Infrastructure and Technology
- Understand where the industry currently stands with regards to machine learning applications in finance from a quantitative viewpoint
- Discuss classical and advanced models
- Learn about capabilities of machine learning tools in portfolio construction, trading, risk management and beyond
- Get to grips with modern data analysis - structured and unstructured data and new models
- Understand challenges related to data infrastructure and technology
Director, Artificial Intelligence and Machine Learning
Ajinkya (pronounced A-jink-ya) is a seasoned leader and specializes in commercializing promising technology innovations.
Over his career, he has helped large enterprises and startups build technologies ranging from mobile apps to big data and in latest continuation, Artificial Intelligence in the financial sector.
Starting with early iterations of RPA (Robotic Process Automation) and Log analytics, Ajinkya has delivered business results by applying machine learning in all forms, namely statistical modeling, machine learning, network analytics and most recently, neural networks and deep learning. He has applied his skills across many verticals and major brands, IBM, Accenture, TELUS and Scotiabank.
An Engineer by profession, Ajinkya is a graduate of the Master of Management of Innovation program at the University of Toronto. He is an avid photographer and enjoys learning languages in spare time.
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