A First Course in Model Validation and Model Risk Management explains in step-by-step, practical terms how mathematical models owned by financial institutions are essential to their public (sales, trading, risk management, and internal audit) and private (merger and acquisition, and IPO) activities. Like a diverse fleet of cars maintained by a rental car location, a bank must make sure customers can "drive" any of its models in seeking a profit or hedge in a specific financial product. The book is divided into three sections on conventional pricing and risk models, including risk-neutral and historical measures. Chapters consider modeling basics, marked-to-market asset classes, market risk, credit risk, portfolio risk, operational risk, capital model risk, and financial crime, along with machine learning, AI, and Python specific modeling and risk assessment techniques. Problems sets, video examples, sample Python code, and an instructor manual are offered on companion and instructor sites to support learning and provide an opportunity to put concepts into practice. A refresher in statistics and an abbreviation glossary are included across two appendices.