Technical requirementsIntroduction to AI and machine learningClassification of the most common artificial intelligence techniquesHeuristic searchFirst-order calculusPlannersHeuristic rules, neural networks, and Bayesian networksTruth maintenance systemsWhat is machine learning and why does it work?Machine learning basicsHow many examples do I need to learn my function?Learning smooth functionsNeural networksThe support vector algorithmLearning small computer programs: induction-based learningFrom theory to practice: the role of a software architect in an ML projectStep 1 – Define your goalStep 2 – Provide and prepare dataStep 3 – Train, tune, and deploy your modelStep 4 – Test the trained model and feed back on itML.NET – Machine learning made for .NETML.NET Model BuilderSummaryQuestionsFurther reading