Course Description
Welcome to Deep Learning! Over the past few years, Deep Learning has become a popular area, with deep neural network methods obtaining state-of-the-art results on applications in computer vision (Self-Driving Cars), natural language processing (Google Translate), and reinforcement learning (AlphaGo). These technologies are having transformative effects on our society, including some undesirable ones (e.g. deep fakes).
This course is there to give students a practical understanding of how Deep Learning works, how to implement neural networks, and how to apply them ethically. We introduce students to the core concepts of deep neural networks and survey the techniques used to model complex processes within the contexts of computer vision and natural language processing.
Throughout the course, we emphasize and require students to think critically about potential ethical pitfalls that can result from mis-application of these powerful models. The course is taught using the Tensorflow deep learning framework.
Location
Granoff Ctr for Creative Arts 110
Schedule
Tuesday and Thursday, 9:00-10:20am
Instructor
Prof. Eric Ewing
Most Recent Lecture
Most Recent Assignment
Lectures
Weeks 1-3: Foundations of Neural Networks
- 2025-09-4Welcome to Deep Learning
- 2025-09-9Machine Learning
- 2025-09-11Perceptrons and MLPs
- 2025-09-16Optimization, Gradients, and Losses
- 2025-09-18Backpropagation and SGD
- 2025-09-23Automatic Differentiation and Hyperparameter tuning
Weeks 4-5: Convolutional Neural Networks
- 2025-09-25Convolutional Neural Networks
- 2025-09-30Regularization and Resnet
- 2025-10-2Adversarial Learning
- 2025-10-7Geometric Deep Learning
Weeks 6-8: Learning with Sequential Data
- 2025-10-9Learning with Sequential Data
- 2025-10-14RNNs and LSTMs
- 2025-10-16Seq2Seq
- 2025-10-21Transformers
- 2025-10-23Large Language Models and Generative AI
- 2025-10-28High Performance Computing (Oscar Tutorial)
Weeks 9-11: Unsupervised Learning and Reinforcement Learning
- 2025-10-30Image Generation
- 2025-11-4VAEs and GANs
- 2025-11-6Diffusion Models
- 2025-11-11Reinforcement Learning
- 2025-11-13Policy Gradient Methods
- 2025-11-18PPO
- 2025-11-20Slack Day
- 2025-11-25Slack Day
Weeks 12-13: Looking Forward
- 2025-12-2The Current State of AI
- 2025-12-4The Future of AI
Assignments
Assignment 1: Introduction and Mathematical Foundations
Assignment 2: Introduction to Numpy and Tensorflow
Assignment 3: BERAS
Assignment 4: CNNS
Assignment 5: Language Modeling
Assignment 6: Image Captioning
Assignment 7: Generative Modeling
Assignment 8: Reinforcement Learning
Course Timeline
Resources
Guides and Tutorials
Working Remotely
Department Resources
Expedition Team
Do not email sensitive information, including Health Services & Dean's Notes, to any HTAs, UTAs, or STAs.
Lead Geologist

Eric Ewing
he/him
Senior Excavators

Armaan Patankar
he/him
Mining Specialists

Johnny Elias
he/him

Narek Harutyunyan
he/him

Sreedevi Prasad
she/her

Maria Wang
she/her