The 26th MLSP workshop in the series of workshops organized by the IEEE Signal Processing Society MLSP Technical Committee will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions. Prospective authors are invited to submit papers on relevant algorithms and applications including, but not limited to:

• Bayesian learning and signal processing

• Cognitive information processing

• Deep learning techniques

• Dictionary learning

• Graphical and kernel methods

• Independent component analysis

• Information-theoretic learning

• Learning theory and algorithms

• Pattern recognition and classification

• Bounds on performance

• Subspace and manifold learning

• Sequential learning and decision methods

• Source separation

• Tensor and structured matrix methods

• Machine learning from big data

• Scalable learning algorithms

• Applications including: speech, audio & music, image & video, biomedical signals & images, communications, bioinformatics, biometrics, systems biology, computational intelligence, genomic signals & sequences, social networks, games, smart grid, security & privacy