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Ending remarks for the course

In this video, Prof. Hao Ni summarizes the main contents of this introductory course on machine learning and quantitative finance.

The four-week introductory course has now come to an end. We hope that it can help you build up a theoretical understanding of deep learning and acquire hands-on experience in applying neural networks to financial data challenges in practice.

This course mainly focuses on supervised learning algorithms in finance. However, other major categories of machine learning methods, e.g., unsupervised learning and reinforcement learning mentioned in Step 1.6, are equally important and helpful in quantitative finance. Our book entitled “An Introduction to Machine Learning in Quantitative Finance” provides an introduction to unsupervised learning and reinforcement learning, with example applications on FX interest yield curves and optimal trading problems, respectively. The GitHub codes of the book are provided to enable readers to try out the python codes and see how it works in practice. Due to limited time, we only cover linear regression and basic neural networks (i.e., artificial neural networks and recurrent neural networks) in the course. However, we may encounter many other useful and effective supervised learning methods depending on use cases. For example, tree-based methods are sometimes powerful for empirical data problems. In deep learning, network architecture design is still a significant problem and a hot topic in machine learning applications.

If you are interested in pursuing a career in ML and quantitative finance, you should master major machine learning categories. You should be familiar with the general framework of ML, the representative algorithms of each ML category, and the mathematics and intuition behind them. One effective way of learning new machine learning algorithms is through programming to implement them on your own and applying them to financial data problems. Over time you will master many different algorithms and gradually develop your own insights on different algorithms and their empirical performance.

Participation in Kaggle competitions on financial data applications may also help you acquire practical skills in machine learning. You may learn clever tricks and up-to-date machine learning methods from other participants. If you stand out to win one of the competitive challenges funded by leading hedge funds or banks, it may potentially bring exciting job opportunities to you.

The end of this course is the new start of your journey in ML in quantitative finance. Enjoy your adventure! We wish you all the best in your future career endeavours!

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An Introduction to Machine Learning in Quantitative Finance

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