As Taught in:
2017 – 2018 – 2019 – 2020
Departments:
Electrical Engineering and Computer Science on Artificial Intelligence, Graphics and Visualization, and Software Design and Engineering.
Learning Resource Types:
Lecture Slides
Course Overview:
This introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Learners will gain foundational knowledge on Deep Learning Algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from the community of industry sponsors.
Prerequisites:
Calculus, Linear Algebra, & CS-Python.
Outline the topic from:
Lec 01 – Introduction
Lec 02 – Deep Sequence Modeling
Lec 03 – TensorFlow and Audio Generation
Lec 04 – Deep Computer Vision
Lec 05 – Deep Generative Modeling
Lec 06 – De-biasing Facial Recognition Systems
Lec 07 – Deep Reinforcement Learning
Lec 08 – Limitations and New Frontiers
Lec 09 – Pixels of Control Learning
Lec 10 – Neurosymbolic Hybrid AI
Lec 11 – Generalizable Autonomy in Robotics
Lec 12 – Neural Rendering
Lec 13 – Machine Learning for Scent