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

GRPO and LLMs2025-11-20

Most Recent Assignment

Assignment 7: Reinforcement LearningOut Date: 2025-11-20In Date: 2025-12-04

Lectures

Weeks 1-3: Foundations of Neural Networks

Weeks 4-5: Convolutional Neural Networks

Weeks 6-8: Learning with Sequential Data

Weeks 9-11: Unsupervised Learning and Reinforcement Learning

Weeks 12-13: Looking Forward

  • 2025-12-2End-to-End Learning
  • 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: 2025-10-04

Assignment 4: CNNS

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

Assignment 5: Language Modeling

Out Date: 2025-10-18Conceptual In Date: 2025-10-30Programming In Date: 2025-11-06

Assignment 6: Generative Modeling

Out Date: 2025-11-10Programming In Date: 2025-11-20

Assignment 7: Reinforcement Learning

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

CSCI 1470 Deep Learning Final Project

A semester-long research project where you'll apply deep learning to solve a real problem. Work in teams of 3-4 to either re-implement a research paper or develop a novel solution, gaining hands-on experience in research methodology, experimentation, and presentation.

Deep Learning Day 2025

Celebrate Your Hard Work and Present Your Research!

Date

December 11, 2025

Duration

9:15 AM - 12:15 PM

Location

Third Floor Atrium
Watson Sr. Center for Information Technology (CIT)

Two Presentation Sessions

Click on a session to view group assignments and search for your team

Project Timeline

2025-10-26

Team Formation Deadline

Form your team (3-4 people) or let us assign you a team. Submit the form to indicate your preference.

2025-10-28

Final Team Assignments

Your mentor TA will be assigned. Check for the announcement to see your team composition.

2025-11-03

Project Check-in #1 (Week of Nov 3-7)

Meet with your mentor TA for a brainstorming session. Come prepared with 2-3 project ideas and discuss feasibility and scope.

2025-11-10

Project Proposal Due

Submit your finalized project idea. Late submissions receive a 2% grade deduction.

2025-11-21

Intermediate Project Report Due

Submit a 2-page outline detailing your plan, methodology, data, and GitHub repo link. Include your outline with your mentor TA.

2025-12-01

Project Check-in #2 (Week of Dec 1)

Final check-in meeting with mentor. Submit a one-page reflection on progress, challenges, and next steps.

2025-12-11

Deep Learning Day

Celebrate your work! Present your poster to peers and explore other projects. Prepare a ~2 minute presentation.

2025-12-14

Final Submission Deadline

Hard deadline at 10 PM. Submit your poster (JPG), code (GitHub), and final writeup. No late days allowed.

Course Timeline

Semester Progress
0% CompleteSemester Complete!

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

Sophia Li

Sophia Li

she/her

Jacob Hirschhorn

Jacob Hirschhorn

he/him

Matthew Prenovitz

Matthew Prenovitz

he/him