Brains are biological computing machines that contain billions of small computing units called neurons and trillions of connections known as synapses. Computations arose from networks of activity of these neurons and their connections are thought to underlie the transformation of complex and noisy sensory information into coherent perception, thought, and action. For many decades, the field of machine learning has also been inspired by the biological basis of the brain. However, it has been challenging to build brain-like algorithms that can reach human intelligence. This challenge has driven the emergence of brain-inspired computer vision, advanced neuroscience techniques, and quantitative models that describe links between brains and behaviors. In this class, we will provide an introduction to the neural basis of perception, cognition, and action. Moreover, we will explore cutting-edge neurotechnology essential for advances in neuroscience research and applications. In addition, we will discuss new discoveries in neuroscience and computer science in order to find ways to merge the two fields, making steps closer to building artificial brains. The class will consist of a series of lectures, workshops, and projects related to neuroscience, neurotechnology, and their applications.