Master Deep Learning for Financial Markets
Neural networks are reshaping finance. From portfolio management to risk assessment, deep learning unlocks patterns that traditional methods miss. Our program gives you practical skills to build and deploy models that matter.
Explore Program Details
From Theory to Trading Floors
You'll start with fundamentals but quickly move into real applications. By month three, you're building predictive models. By month six, you're working with actual market data. And by the end? You'll have a portfolio that demonstrates your ability to solve complex financial problems with machine learning.
Design recurrent neural networks that detect market sentiment shifts from news feeds and social data streams
Build reinforcement learning agents that adjust portfolio allocations based on changing market conditions
Create credit risk models using gradient boosting and deep learning that improve loan approval accuracy
Deploy fraud detection systems that process transaction patterns in real time using convolutional networks
Real Skills, Tangible Results
We focus on capabilities that translate directly into professional value. These aren't hypothetical scenarios—they're drawn from what our participants have built after completing the program.
Portfolio Optimization
Learn to build models that balance risk and return across diverse asset classes. You'll work with modern portfolio theory enhanced by neural network predictions.
Recent participants developed multi-asset allocation systems that adjust positions based on volatility forecasts from LSTM networks, reducing drawdown periods significantly.
Algorithmic Trading Strategies
Create automated trading systems that identify entry and exit points using technical patterns and market microstructure data.
One participant built a momentum strategy using convolutional networks to process candlestick patterns, achieving consistent performance across different market regimes during backtesting.
Credit Assessment Models
Develop lending decision systems that evaluate creditworthiness using both traditional metrics and alternative data sources.
A fintech analyst in our program created a model combining transaction history with behavioral data, improving loan approval accuracy while maintaining risk thresholds.
Market Sentiment Analysis
Build systems that process news articles, earnings calls, and social media to gauge market sentiment and predict price movements.
Several participants have implemented sentiment trackers using transformer models that correlate news flow with subsequent price action, providing early warning signals for portfolio managers.
How Learning Actually Works Here
Live Problem Sessions
Every week, you'll join sessions where we tackle actual financial datasets together. Someone brings a problem—maybe it's overfitting in a time series model or handling imbalanced fraud data—and we work through solutions as a group.
This isn't lecture-based learning. You're actively debugging, testing hypotheses, and seeing what works in practice. By month four, participants often lead these sessions themselves.
Project Mentorship
You'll work on three major projects throughout the program. Each one gets individual feedback from instructors who've built production systems in finance. They'll review your code, question your assumptions, and push you to think deeper about model validation.
Technical Infrastructure
Access to cloud computing resources for training large models, along with curated financial datasets that most practitioners never see. You'll work with the same tools used at hedge funds and banks.
View Setup DetailsWhen Programs Start
Our next cohort begins in January 2026. We run two programs annually—one starting in January and another in July. Class sizes stay small, typically 15-20 participants, so everyone gets meaningful interaction time.
Applications for January 2026 open in September 2025. The selection process includes a technical assessment and conversation about your background and goals. We're looking for people with some programming experience and genuine interest in both finance and machine learning.
What surprised me most was how quickly theory became practice. Within two months, I was building volatility forecasting models that actually worked. The instructors didn't just teach concepts—they showed us why certain approaches fail in production and how to build systems that stay robust when markets shift.
Kasper joined the program in 2024 with a background in traditional quantitative analysis. He's now developing machine learning infrastructure at a Bangkok-based investment firm, focusing on emerging market opportunities in Southeast Asia.
Ready to Start Building?
Our next program begins in early 2026. If you're serious about applying machine learning to finance and want structured, hands-on training, let's talk about whether this fits your goals.