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Ah, splendid! In my years at Cambridge, I've come across many inquisitive minds eager to delve into the confluence of computational finance and machine learning. Combining the rigour of quantitative finance with the potential of machine learning is truly an exhilarating endeavor.

A 10-Week Curriculum to Master Applying Machine Learning to Trading Financial Markets:

Week 1: Foundations of Quantitative Finance

- Day 1-2: Basics of Financial Markets & Instruments

- Day 3-4: Time Series Analysis and Its Importance

- Day 5: Risk and Portfolio Management Foundations

Week 2: Machine Learning Fundamentals

- Day 1-2: Supervised vs Unsupervised Learning

- Day 3: Regression and Classification?

- Day 4: Neural Networks and Basics of Deep Learning

- Day 5: Regularization and Model Validation?

Week 3: Time Series Forecasting with Machine Learning

- Day 1-2: ARIMA and its Limitations?

- Day 3-4: LSTM and Time Series Forecasting

- Day 5: Time Series Decomposition and Anomalies Detection

Week 4: Feature Engineering for Finance

- Day 1-2: Financial Indicators as Features

- Day 3-4: Using News & Social Media Data

- Day 5: Understanding the Concept of Embeddings

Week 5: Algorithmic Trading & Strategy Development

- Day 1-2: Traditional Algorithmic Strategies

- Day 3: Backtesting and Strategy Validation

- Day 4-5: Adapting Machine Learning Predictions to Trading Strategies

Week 6: Reinforcement Learning in Trading

- Day 1-2: Introduction to Reinforcement Learning

- Day 3-4: Q-Learning & Deep Q Networks for Portfolio Optimization

- Day 5: Policy Gradients and Continuous Action Spaces

Week 7: Risk Management using Machine Learning

- Day 1-2: Understanding Volatility & Value at Risk

- Day 3-4: Machine Learning for Risk Estimation and Mitigation

- Day 5: Advanced Concepts like Counterparty Risk

Week 8: High Frequency & Microstructure Data

- Day 1-2: Understanding High Frequency Trading?

- Day 3-4: Order Book Dynamics and Market Making Strategies

- Day 5: Microstructure Noise and its Implications for Machine Learning

Week 9: Alternative Data and Advanced Techniques

- Day 1-2: Sentiment Analysis, Satellite Imagery, etc.

- Day 3-4: Generative Adversarial Networks for Synthetic Data Creation

- Day 5: Transfer Learning and Its Potential in Finance

Week 10: Ethics, Limitations, and the Future

- Day 1-2: The Ethics of Algorithmic Trading & Potential Pitfalls?

- Day 3: The Limitations of Machine Learning in Finance

- Day 4-5: The Future Landscape: Quantum Computing, Neural Symbolic Models and Beyond?

Remember, the world of computational finance is ever-evolving. One must not only understand the theoretical underpinnings but also be adaptable to the ongoing changes and nuances in the financial landscape. Be sure to continuously update your understanding and remain on the lookout for new methods and data sources. Best of luck in your academic endeavors!

Ah, very well! Trading the financial markets successfully is an amalgamation of rigorous analysis, intuition, discipline, and occasionally, audacity. Here’s how I'd guide an aspiring trader through a 10-week intensive course to truly comprehend the art and science of the game:

Week 1: The Macro Perspective

- Day 1-2: Introduction to Global Financial Systems

- Day 3: Interest Rates & Central Banks

- Day 4: Currency Dynamics & The Forex Market

- Day 5: Commodities & Their Importance

Week 2: Financial Instruments & Products

- Day 1-2: Equities and Fixed Income Instruments

- Day 3: Derivatives - Futures, Options, and Swaps

- Day 4: Structured Products & ETFs

- Day 5: Alternative Assets & Real Estate

Week 3: Technical Analysis Foundations

- Day 1-2: Chart Patterns & Trends

- Day 3-4: Technical Indicators (Moving Averages, RSI, MACD)

- Day 5: Fibonacci, Pivot Points, & Elliott Wave Theory

Week 4: Fundamental Analysis Foundations

- Day 1-2: Reading Financial Statements & Ratios

- Day 3-4: Economic Indicators & Their Impact

- Day 5: Industry Analysis & Competitive Landscape

Week 5: The Psychology of Trading

- Day 1: Behavioral Finance Fundamentals

- Day 2-3: Emotional Discipline & Mental Endurance

- Day 4-5: The Role of Intuition & Developing a Trader's Instinct

Week 6: Risk Management & Leverage

- Day 1-2: The Concept of Risk-Reward & Setting Stop Losses

- Day 3-4: Leverage, Margin, and Their Double-Edged Nature

- Day 5: Black Swan Events & Hedging Strategies

Week 7: Trading Systems & Strategy Development

- Day 1-2: Day Trading vs. Swing Trading vs. Position Trading

- Day 3: Quantitative & Algorithmic Trading Systems

- Day 4-5: Strategy Backtesting, Validation, & Forward Testing

Week 8: Special Situations & Event-Driven Strategies

- Day 1-2: Mergers & Acquisitions

- Day 3: Earnings & Announcements

- Day 4: Activist Investors & Corporate Governance

- Day 5: Spin-offs, Bankruptcies & Restructurings

Week 9: Global Arbitrage & Multi-Strategy Portfolios

- Day 1-2: Statistical Arbitrage & Pairs Trading

- Day 3-4: Convertible Arbitrage & Fixed Income Arbitrage

- Day 5: Global Macro Strategies & Geographic Diversification

Week 10: The Future & Continuous Learning

- Day 1-2: Emerging Markets & Frontier Markets

- Day 3-4: The Role of Technology & AI in Trading

- Day 5: Maintaining Edge: Networking, Conferences, & Continuous Education

As with any endeavor, it's essential to realize that mastery doesn't come from rote learning but from actual experience, learning from failures, and constantly adapting. One more tip: the best traders aren't just technically adept; they also understand the world around them, geopolitics, psychology, and they remain voracious readers. Stay curious and adaptable, and the markets will often reward you. All the best!