Course Info Syllabus Topic Course Info

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

Syllabus

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.

Topic

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