Frequently Asked Questions

You don't need to be a programmer, but familiarity with basic Python is helpful. We cover implementation fundamentals step-by-step, so you can follow along even if you're new to coding. The focus is on understanding concepts and applying them to portfolio management, not becoming a software developer.

We focus specifically on how machine learning and reinforcement learning can improve allocation decisions. Rather than just teaching rebalancing rules, we show you how adaptive systems learn from market data and adjust strategies in real time. This bridges the gap between academic theory and what you actually need in professional portfolio management.

Most participants complete the material over 6-8 weeks, dedicating around 5-8 hours per week. You can work through it at your own pace, so you might finish faster or spread it out longer depending on your schedule and how deep you want to go with practical exercises.

Yes. We focus on practical frameworks you can test in your own environment. You'll build a working model as part of the course and see how adaptive allocation responds to real market data. The challenge is always in implementation details specific to your setup, but we give you the tools to figure those out.

We offer both online and in-person sessions in Edmonton. Online learners get full access to recorded material, code examples, and live office hours where you can ask questions directly. Choose whatever format works best for your schedule and learning style.

That's a fair question. Part of the course is understanding when adaptive strategies make sense and when traditional approaches work better. We're honest about the trade-offs: reinforcement learning requires more data and monitoring, but it can adapt to changing market conditions in ways static rules can't.