This course continues the learning of discrete choice models introduced in the course "Predictive Analytics". In particular, we focus on models and algorithms that improve the predictive quality of classic discrete choice models. For example, nested, cross-nested logit models, MEV models, probit models, and mixed logit. Further, we discuss how attitudes, perceptions etc impact the choice behavior of individuals and how we can account for these in modern (hybrid) choice models.