Lecture
This lecture covers the fundamental components of machine learning algorithms, including input description, internal representation, and decision mechanisms. It delves into feature selection techniques such as FAST and BRIEF descriptors, emphasizing the importance of selecting relevant features in large datasets. The instructor also discusses the limitations of deep learning, highlighting issues like algorithmic bias, lack of diversity, and susceptibility to deception, urging the audience to explore a variety of machine learning techniques beyond deep learning for a more comprehensive understanding of the field.