將數位資料特徵化，並從中識別感興趣的特徵和模式的基本概念。信號理解問題的基本工具和理論，及其在用戶模型，情感識別，語音識別與理解，電腦視覺，生理分析等方面的應用。決策理論，統計分類，最大似然和貝葉斯估計，非參數方法，非監督的學習與聚類。補充課題中會涉及人機學習領域中當前活躍的研究內容。

Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.