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

Machine Learning2025-09-9

Most Recent Assignment

Assignment 1: Introduction and Mathematical FoundationsOut Date: 2025-09-04In Date: 2025-09-18

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

Out Date: 2025-09-04Conceptual In Date: 2025-09-18

Assignment 2: Introduction to Numpy and Tensorflow

Out Date: 2025-09-11Programming In Date: 2025-09-18

Assignment 3: BERAS

Out Date: 2025-09-18Conceptual In Date: 2025-09-25Programming In Date: 2025-10-02

Assignment 4: CNNS

Out Date: 2025-10-02Conceptual In Date: 2025-10-09Programming In Date: 2025-10-16

Assignment 5: Language Modeling

Out Date: 2025-10-16Conceptual In Date: 2025-10-23Programming In Date: 2025-10-30

Assignment 6: Image Captioning

Out Date: 2025-10-30Conceptual In Date: 2025-11-06Programming In Date: 2025-11-13

Assignment 7: Generative Modeling

Out Date: 2025-11-13Programming In Date: 2025-11-20

Assignment 8: Reinforcement Learning

Out Date: 2025-11-20Conceptual In Date: 2025-12-04Programming In Date: 2025-12-04

Course Timeline

Semester Progress
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Resources

Expedition Team

Do not email sensitive information, including Health Services & Dean's Notes, to any HTAs, UTAs, or STAs.

Lead Geologist

Eric Ewing

Eric Ewing

he/him

Senior Excavators

Armaan Patankar

Armaan Patankar

he/him

Mining Specialists

Johnny Elias

Johnny Elias

he/him

Narek Harutyunyan

Narek Harutyunyan

he/him

Sreedevi Prasad

Sreedevi Prasad

she/her

Maria Wang

Maria Wang

she/her