Machine Learning, Animated

Mark H. Liu is Associate Professor of Finance, (Founding) Director of MS Finance Program, University of Kentucky. Mark is currently the director of Master of Science in Finance program at the University of Kentucky, U.S.A. He is also an associate professor of finance with tenure at the University of Kentucky. He obtained his Ph.D. in finance from Boston College in 2004 and his M.A. in economics from Western University in Canada in 1998. His research interest is in machine learning and corporate finance. He has published his research in top finance journals such as Journal of Financial Economics, Journal of Financial and Quantitative Analysis, Journal of Corporate Finance, and Review of Corporate Finance Studies. Dr. Mark Liu has run Python workshops for master students at the University of Kentucky in the last few years. He has incorporated Python in his teaching. In particular, he is now teaching a Python Predictive Analytics course to graduate students. As the director of the MS Finance program, Mark has seen first-hand the high demand for machine learning skills in all industries. He has interacted with executives and recruiters from hundreds of companies, who in recent years have put an increasing emphasis on the importance of incorporating machine learning and data analytics skills in all business fields.

• List of FiguresPrefaceSection I Installing Python and Learning Animations1. Installing Anaconda and Jupyter Notebook2. Creating AnimationsSection II Machine Learning Basics3. Machine Learning: An Overview4. Gradient Descent - Where the Magic Happens5. Introduction to Neural Networks6. Activation FunctionsSection III Binary and Multi-Category Classifications7. Binary Classifications8. Convolutional Neural Networks9. Multi-Category Image ClassificationsSection IV Developing Deep Learning Game Strategies10. Deep Learning Game Strategies11. Deep Learning in the Cart Pole Game12. Deep Learning in Multi-Player Games13. Deep Learning in Connect FourSection V Reinforcement Learning14. Introduction to Reinforcement Learning15. Q-Learning with Continuous States16. Solving Real-World Problems with Machine LearningSection VI Deep Reinforcement Learning17. Deep Q-Learning18. Policy-Based Deep Reinforcement Learning19. The Policy Gradient Method in Breakout20. Double Deep Q-Learning21. Space Invaders with Double Deep Q-Learning22. Scaling Up Double Deep Q-LearningBibliography