This course aims at giving an overview of two selected topics in Machine Learning which are feature analysis and unsupervised learning. Feature analysis comprises approaches for signal representation and discrimination based on feature space transformations. Classical methods of feature selection and extraction will be reviewed. The part related to Unsupervised learning will stem from approaches related to feature representation and review other techniques such as clustering and mixture models. Some examples of applications in remote sensing will be provided.