Build deep learning models using CNNs, RNNs, and industry tools like TensorFlow and PyTorch.
Deep learning, a branch of artificial intelligence and machine learning, uses multilayered neural networks to create highly accurate prediction models for image recognition, object detection, language translation, speech recognition, and other tasks. In this course, students will use open source and industry-standard machine learning libraries to build and deploy deep learning models.
Students will build deep learning prediction models of different complexities, from simple linear logistic regression to major categories of neural networks including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTMs), and gated recurrent units (GRUs).
By the end of the course, students will be proficient in best practices of using standard machine learning frameworks such as Pytorch, TensorFlow and Keras, and using datasets for solving common machine learning problems.
The class prepares students to pursue a career in data sciences and AI model development.
Note(s):
Students are required to bring laptops for the classroom and work with Python3/
Jupyter Notebook environment.
Moderate level of computer programming ability in Python, comfortable with
an editor, familiarity with command-line operations on a laptop, and a basic understanding
of Machine Learning models.
This class meets simultaneously in a classroom and remotely via Zoom. Students are expected to attend and participate in the course, either in-person or remotely, during the days and times that are specified on the course schedule. Students attending remotely are also strongly encouraged to have their cameras on to get the most out of the remote learning experience. Students attending the class in-person are expected to bring a laptop to each class meeting.
No meetings on January 19 and February 16, 2026. To see all meeting dates, click “Full Schedule” below.
You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.
Recommended Text:
"Deep Learning", Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press 2016 ISBN# 978-0262035613. A free e-book is available at http://www.deeplearningbook.org (Links to an external site.)
