MIT 6.S191: Introduction to Deep Learning

Introduction to Deep Learning is a course offered by the Massachusetts Institute of Technology (MIT) under the course code MIT 6.S191. It is an undergraduate-level course aimed at introducing students to the fundamentals of deep learning, a rapidly growing subfield of machine learning. The course begins with an overview of the history of artificial intelligence and its recent resurgence, followed by an introduction to neural networks, the building blocks of deep learning. Students will learn about the structure and functionality of various types of neural networks, such as feedforward networks, convolutional networks, and recurrent networks. The course covers the mathematics behind neural networks, including linear algebra and calculus, as well as optimization techniques used in training deep learning models. Students will also learn about the Python programming language and the popular deep learning library TensorFlow. Through a series of lectures, tutorials, and assignments, students will gain practical experience in implementing and training deep learning models for tasks such as image classification, natural language processing, and speech recognition. They will also learn how to evaluate and analyze the performance of these models and how to avoid common pitfalls and mistakes. The course includes several guest lectures by experts in the field of deep learning, providing students with the opportunity to learn about cutting-edge research and real-world applications of the technology. There will also be hands-on projects and exercises, allowing students to apply what they have learned and develop their own deep learning models. By the end of the course, students will have a solid understanding of the fundamentals of deep learning and the ability to implement and train their own deep learning models for a variety of applications. This course is ideal for students interested in pursuing further studies in machine learning or data science or for professionals seeking to expand their knowledge and skills in this rapidly growing field. Author: Alexander Amini, Ava Soleimany