Human face-to-face communication is a little like a dance, in that participants continuously adjust their behaviors based on verbal and nonverbal displays and signals. Human interpersonal behaviors have long been studied in linguistic, communication, sociology and psychology. The recent advances in machine learning, pattern recognition and signal processing enabled a new generation of computational tools to analyze, recognize and predict human communication behaviors during social interactions. This new research direction have broad applicability, including the improvement of human behavior recognition, the synthesis of natural animations for robots and virtual humans, the development of intelligent tutoring systems, and the diagnoses of social disorders (e.g., autism spectrum disorder). This is a graduate course primarily for students in LTI, HCII and Robotics; others, for example (undergraduate) students of CS or professional masters, by prior permission of the instructor. Students should have proper academic background in probability, statistic and linear algebra. Previous experience in machine learning is suggested but not obligatory. Programming knowledge in Matlab and/or Python is recommended.