Andrea Mechelli is a clinical psychologist and a neuroscientist with an interest in the early detection and treatment of mental illness. After studying Psychology at the University of Padua (1999), he completed a PhD in Neurological Sciences at University College London in 2002 and became an academic member of staff at King's College London in 2004. He currently holds the position of Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience at King's College London. Prof. Mechelli's research involves the application of advanced machine learning methods to clinical, neuroimaging and smartphone data, with the aim of developing and validating novel tools for early detection and treatment. Sandra Vieira is a postdoctoral researcher at the Institute Psychiatry, Psychology & Neuroscience (King's College London). After completing a degree in Psychology (2009) and a Masters in Clinical Psychology (2011) at the University of Coimbra, she joined the Institute Psychiatry, Psychology & Neuroscience. Here she obtained a Masters in Psychiatric Research in 2014 and a PhD in Psychosis Studies in 2019. Her research focuses on the integration of advanced machine learning methods and multi-modal neuroimaging to investigate the neural basis of mental illness and develop imaging-based clinical tools.

"This is a fantastic resource for researchers and clinicians interested in the application of artificial intelligence to brain disorders. The most up-to-date approaches are covered, using a rigorous yet accessible language. The step-by-step practical guide will be particularly useful to those taking their first steps in this field." --Qiyong Gong, MD, PhD

• Part I1. Introduction to machine learning2. Main concepts in machine learning3. Applications of machine learning to brain disordersPart II4. Linear regression5. Linear methods for classification6. Support vector machine7. Support vector regression8. Multiple kernel learning 9. Deep neural networks10. Convolutional neural networks11. Autoencoders12. Principal component analysis13. K-means clusteringPart III14. Dealing with missing data, small sample sizes, and heterogeneity15. Working with high dimensional feature spaces: the example of voxel-wise encoding models16. Multimodal integration17. Bias, noise and interpretability in machine learning: from measurements to features18. Ethical issues in the application of machine learning to brain disordersPart IV19. A step-by-step tutorial on how to build a machine learning model

Andrea Mechelli is a clinical psychologist and a neuroscientist with an interest in the early detection and treatment of mental illness. After studying Psychology at the University of Padua (1999), he completed a PhD in Neurological Sciences at University College London in 2002 and became an academic member of staff at King's College London in 2004. He currently holds the position of Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience at King's College London. Prof. Mechelli's research involves the application of advanced machine learning methods to clinical, neuroimaging and smartphone data, with the aim of developing and validating novel tools for early detection and treatment. Sandra Vieira is a postdoctoral researcher at the Institute Psychiatry, Psychology & Neuroscience (King's College London). After completing a degree in Psychology (2009) and a Masters in Clinical Psychology (2011) at the University of Coimbra, she joined the Institute Psychiatry, Psychology & Neuroscience. Here she obtained a Masters in Psychiatric Research in 2014 and a PhD in Psychosis Studies in 2019. Her research focuses on the integration of advanced machine learning methods and multi-modal neuroimaging to investigate the neural basis of mental illness and develop imaging-based clinical tools.

"This is a fantastic resource for researchers and clinicians interested in the application of artificial intelligence to brain disorders. The most up-to-date approaches are covered, using a rigorous yet accessible language. The step-by-step practical guide will be particularly useful to those taking their first steps in this field." --Qiyong Gong, MD, PhD

• Part I1. Introduction to machine learning2. Main concepts in machine learning3. Applications of machine learning to brain disordersPart II4. Linear regression5. Linear methods for classification6. Support vector machine7. Support vector regression8. Multiple kernel learning 9. Deep neural networks10. Convolutional neural networks11. Autoencoders12. Principal component analysis13. K-means clusteringPart III14. Dealing with missing data, small sample sizes, and heterogeneity15. Working with high dimensional feature spaces: the example of voxel-wise encoding models16. Multimodal integration17. Bias, noise and interpretability in machine learning: from measurements to features18. Ethical issues in the application of machine learning to brain disordersPart IV19. A step-by-step tutorial on how to build a machine learning model

Andrea Mechelli is a clinical psychologist and a neuroscientist with an interest in the early detection and treatment of mental illness. After studying Psychology at the University of Padua (1999), he completed a PhD in Neurological Sciences at University College London in 2002 and became an academic member of staff at King's College London in 2004. He currently holds the position of Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience at King's College London. Prof. Mechelli's research involves the application of advanced machine learning methods to clinical, neuroimaging and smartphone data, with the aim of developing and validating novel tools for early detection and treatment. Sandra Vieira is a postdoctoral researcher at the Institute Psychiatry, Psychology & Neuroscience (King's College London). After completing a degree in Psychology (2009) and a Masters in Clinical Psychology (2011) at the University of Coimbra, she joined the Institute Psychiatry, Psychology & Neuroscience. Here she obtained a Masters in Psychiatric Research in 2014 and a PhD in Psychosis Studies in 2019. Her research focuses on the integration of advanced machine learning methods and multi-modal neuroimaging to investigate the neural basis of mental illness and develop imaging-based clinical tools.

"This is a fantastic resource for researchers and clinicians interested in the application of artificial intelligence to brain disorders. The most up-to-date approaches are covered, using a rigorous yet accessible language. The step-by-step practical guide will be particularly useful to those taking their first steps in this field." --Qiyong Gong, MD, PhD

• Part I1. Introduction to machine learning2. Main concepts in machine learning3. Applications of machine learning to brain disordersPart II4. Linear regression5. Linear methods for classification6. Support vector machine7. Support vector regression8. Multiple kernel learning 9. Deep neural networks10. Convolutional neural networks11. Autoencoders12. Principal component analysis13. K-means clusteringPart III14. Dealing with missing data, small sample sizes, and heterogeneity15. Working with high dimensional feature spaces: the example of voxel-wise encoding models16. Multimodal integration17. Bias, noise and interpretability in machine learning: from measurements to features18. Ethical issues in the application of machine learning to brain disordersPart IV19. A step-by-step tutorial on how to build a machine learning model