This course will develop basic knowledge and techniques in the various aspects of Machine Learning, Artificial Intelligence, neural networks and deep learning. It will cover the theory behind it paired with practical examples and will walk students through hands-on applications. The course will also include business applications. Students will understand what it takes to integrate and implement machine learning in business and its applications in different fields.
Instructor, GGSJ Centre & MMF
Alik combines a pragmatic approach to data science with deep industry and domain knowledge, statistical rigour and innovate machine learning tools to connect and unlock value in structured and unstructured data.
Alik has applied machine learning across a wide range of business problems, including customer acquisition and retention, segmentation, fraud, pricing and risk, productivity optimization, and in helping structure text data such as social media, customer complaints, and claims notes.
He has previously spent 6 years building algorithms and leading machine learning and product development initiatives in Deloitte Canada’s AI Practice. Alik also holds a HBSc degree in Financial Mathematics from the University of Toronto, and a Master’s Degree in Mathematics from the University of Toronto. He has also completed his CFA designation.
Alik is also currently teaching a Machine Learning course at the Master’s of Mathematical Finance program at the University of Toronto, as well as teaching, participating in workshops and speaking on machine learning and its FSI applications globally.
Alik also heads up the machine learning-driven research projects at RiskLab as a director of machine learning, which research focusing on applications of modern deep learning to classical and novel quantitative finance problems.