Syllabus

For all "Materials and Assignments", follow the deadlines listed on this page, not on Coursera! Assignments are due every Tuesday by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted.

Announcements

  • Please join Ed during the first week. This is where the majority of course announcements will be found.
  • Video cameras located in the back of the room will capture the instructor presentations in this course. For your convenience, you can access these recordings by logging into the course Canvas site. These recordings might be reused in other Stanford courses, viewed by other Stanford students, faculty, or staff, or used for other education and research purposes. Note that while the cameras are positioned with the intention of recording only the instructor, occasionally a part of your image or voice might be incidentally captured. If you have questions, please contact a member of the teaching team.
  • If you would like to audit the course, please fill out the following request form.

Syllabus

  • Coursera. Please use the course invitations sent to your Stanford email to access your courses.
  • Modules are equivalent to “Weeks” in the Coursera courses. For example, C1M1 refers to C1 Week 1.
  • Note that the in-class lecture topics are subject to change as the quarter progresses.
Event Date In-class lecture Online modules to complete Materials and Assignments
Lecture 1 9/22/2026 Topics: (slides)
  • Class introduction
 No online modules. If you are enrolled in CS230, you will receive an email to join Course 1 ("Neural Networks and Deep Learning") on Coursera with your Stanford email. No assignments.
Neural Networks and Deep Learning (Course 1)
Friday Section 1 9/25/2026 Final Project Process & Expectations Recording: Available on Canvas
Slides:Section 1 (Fall 2026).pptx
Lecture 2 9/29/2026 Topics: Key AI Concepts Through Case Studies (slides) Completed modules:
  • C1M1: Introduction to deep learning (slides)
  • C1M2: Neural Network Basics (slides)
Optional Video
  • Batch Normalization videos from C2M3 will be useful for the in-class lecture.
 Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Introduction to deep learning
  • Neural Networks Basics
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted)
  • Python Basics with Numpy (Optional)
  • Logistic Regression with a neural network mindset
Friday Section 2 10/2/2026 Recording: Available on Canvas
Worksheet
Slides
Lecture 3 10/6/2026 Topics: Full Cycle of a DL Project Completed modules: Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Shallow Neural Networks
  • Key concepts on Deep Neural Networks
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Planar data classification with a hidden layer
  • Building your Deep Neural Network: step by step
  • Deep Neural Network — Application
Friday Section 3 10/9/2026 Slide
Project Proposal Due 10/13/2026 (due 11:59 pm PT) Instructions
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Course 2)
Lecture 4 10/13/2026 Topics: Adversarial Robustness and Generative Models (slides) Completed modules:
  • C2M1: Practical aspects of deep learning (slides)
  • C2M2: Optimization algorithms (slides)
 Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Practical aspects of deep learning
  • Optimization Algorithms
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Initialization
  • Regularization
  • Gradient Checking
  • Optimization
Structuring Machine Learning Projects (Course 3)
Friday Section 4 10/16/2026 Slides
Lecture 5 10/20/2026 Topics: DL Strategy Completed modules:
  • C2M3: Hyperparameter Tuning, Batch Normalization (slides)
  • C3M1: ML Strategy (1) (slides)
  • C3M2: ML Strategy (2) (slides)
 Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Hyperparameter tuning, Batch Normalization, Programming Frameworks
  • Bird recognition in the city of Peacetopia (case study)
  • Autonomous driving (case study)
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • TensorFlow
Convolutional Neural Networks (Course 4)
Friday Section 5 10/23/2026 Slides
Lecture 6 10/27/2026 Topics: Technical Foundations of Responsible AI: Interpretability, Fairness, and LLM Governance Completed modules:
  • C4M1: Foundations of Convolutional Neural Network (slides)
  • C4M2: Deep Convolutional Models (slides)
 Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • The basics of ConvNets
  • Deep convolutional models
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Convolutional Model, step by step
  • Convolutional Model Application
  • Residual Networks
  • Transfer Learning with MobileNet
Friday Section 6 10/30/2026 Midterm Review Slides/Recording: TBD
No Lecture 11/3/2026 (Democracy Day - NO CLASS) Democracy day: NO CLASS Completed modules:
  • C4M3: ConvNets Applications (1) (slides)
  • C4M4: ConvNets Applications (2) (slides)
 Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Detection Algorithms
  • Special Applications: Face Recognition & Neural Style Transfer
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Car Detection with YOLO
  • Art Generation with Neural Style Transfer
  • Face Recognition
  • Image Segmentation with U-Net
Midterm 11/4/2026 6:00–9:00 PM, in person. 3 hours, pen and paper format. Attendance is mandatory. There will be no make-up exams.
Sequence Models (Course 5)
Friday Section 7 11/6/2026 Slides/Recording: TBD
Lecture 7 11/10/2026 Topics: Reinforcement Learning and the Role of RL in Modern AI Completed modules:
  • C5M1: Recurrent Neural Networks (slides)
 Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Recurrent Neural Networks
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Building a Recurrent Neural Network — Step by Step
  • Dinosaur Land — Character-level Language Modeling
  • Jazz improvisation with LSTM
Project Milestone Due 11/10/2026 (due 11:59 pm PST) Instructions
Friday Section 8 11/13/2026 Slides/Recording: TBD
Lecture 8 11/17/2026 Topics: Frontiers in AI Applications: Agents, RAG, and Multimodality Completed modules:
  • C5M2: Natural Language Processing and Word Embeddings (slides)
  • C5M3: Sequence-to-Sequence Models (slides)
 Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Natural Language Processing and Word Embeddings
  • Sequence Models and Attention Mechanism
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Operations on Word Vectors — Debiasing
  • Emojify!
  • Neural Machine Translation with Attention
  • Trigger Word Detection
Friday Section 9 11/20/2026 Slides/Recording: TBD
No Lecture 11/24/2026 Thanksgiving Recess
Lecture 9 12/1/2026 Topics: Career Advice + Class Wrap Completed modules: Quizzes (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Transformers
Programming Assignments (due by 8:30 a.m. Pacific Time, 30 minutes prior to the start of lecture time, unless otherwise noted):
  • Transformers Architecture with TensorFlow
Project Final Report Due 12/4/2026 (due 11:59 pm PST) Instructions Please read over the final project guidelines here for information on the rubric and late submissions.
Project Poster Session 12/9/2026 (8:30–11:30 AM PST)