Syllabus

For all "Materials and Assignments", follow the deadlines listed on this page, not on Coursera! Assignments are usually due every Tuesday, 30min before the class starts.

Event Date In-class lecture Online modules to complete Materials and Assignments
Neural Networks and Deep Learning (Course 1)
Lecture 1 04/02 Topics: No online modules. If you are enrolled in CS230, you will receive an email on 04/01 to join Course 1 ("Neural Networks and Deep Learning") on Coursera with your Stanford email. No assignments.
Lecture 2 04/09 Topics: Full-cycle of a deep learning project Completed modules:
  • C1M1: Introduction to deep learning (slides)
  • C1M2: Neural Network Basics (slides)
Quizzes (due 04/09 at 8:30am):
  • Introduction to deep learning
  • Neural Networks Basics
Programming Assignments (due 04/09 at 8:30am)
  • Python Basics with Numpy (Optional)
  • Logistic Regression with a neural network mindset
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Course 2)
Lecture 3 04/16 Topics: Deep Learning Intuition
  • How to frame a machine learning problem?
  • How to choose your loss function?
  • Intuition behind various real-world application of deep learning.
  • Slides
Completed modules: Quizzes (due at 8:30am):
  • Shallow Neural Networks
  • Key concepts on Deep Neural Networks
Programming Assignments (due at 8:30am):
  • Planar data classification with a hidden layer
  • Building your Deep Neural Network: step by step
  • Deep Neural Network - Application
Project Proposal Due 04/16
Tuesday
11:59PM
Instructions
Lecture 4 04/23 Topics:
  • Attacking neural networks with Adversarial Examples and Generative Adversarial Networks (slides)
Optional Readings: Explaining and Harnessing Adversarial Examples, Generative Adversarial Nets, Conditional GAN, Super-Resolution GAN, CycleGAN
Completed modules:
  • C2M1: Practical aspects of deep learning (slides)
  • C2M2: Optimization algorithms (slides)
Optional Video
  • Batch Normalization videos from C2M3 will be useful for the in-class lecture.
Quizzes (due at 8:30am):
  • Practical aspects of deep learning
  • Optimization Algorithms
Programming Assignments (due at 8:30am):
  • Initialization
  • Regularization
  • Gradient Checking
  • Optimization
Structuring Machine Learning Projects (Course 3)
Lecture 5 04/30 Topics: Deep Learning Project strategy - Case studies Completed modules:
  • C2M3: Hyperparameter Tuning, Batch Normalization (slides)
  • C3M1: ML Strategy (1) (slides)
  • C3M2: ML Strategy (2) (slides)
Quizzes (due at 8:30am):
  • Hyperparameter tuning, Batch Normalization, Programming Frameworks
  • Bird recognition in the city of Peacetopia (case study)
  • Autonomous driving (case study)
Programming Assignments (due at 8:30am):
  • Tensorflow
Lecture 6 05/07 Topics:
  • AI in Health Care (Guest speaker: Pranav Rajpurkar)
  • Live-cell segmentation Case Study by Kian Katanforoosh
  • Slides
Completed modules:
  • C4M1: Foundations of Convolutional Neural Network (slides)
  • C4M2: Deep Convolutional Models (slides)
Quizzes (due at 8:30am):
  • The basics of ConvNets
  • Convolutional models
Programming Assignments (due at 8:30am):
  • Convolutional Neural Network - Step by Step
  • Convolutional Neural Network - Application
  • Keras Tutorial: This assignment is optional.
  • Residual Networks
Midterm Review 05/08 Past midterms:
Convolutional Neural Networks (Course 4)
Midterm 05/09 Midterm Alternate Midterm
(Only for students with valid, approved reason)
  • Date: Friday, May 10th
  • Time: 6pm-9pm
  • Location: TBD
Lecture 7 05/14 Topics: Interpretability of Neural Network (slides)

Optional Reading: A guide to convolution arithmetic for deep learning, Is the deconvolution layer the same as a convolutional layer?, Visualizing and Understanding Convolutional Networks, Deep Inside Convolutional Networks: Visualizing Image Classification Models and Saliency Maps, Understanding Neural Networks Through Deep Visualization, Learning Deep Features for Discriminative Localization
Completed modules:
  • C4M3: ConvNets Applications (1) (slides)
  • C4M4: ConvNets Applications (2) (slides)
Quizzes (due at 8:30am):
  • Detection Algorithms
  • Special Applications: Face Recognition and Neural Style Transfer
Programming Assignments (due at 8:30am):
  • Car Detection with YOLOv2
  • Art Generation with Neural Style Transfer
  • Face recognition for the Happy House
Project Milestone Due Tuesday 05/14 at 11:59PM Instructions
Sequence Models (Course 5)
Lecture 8 05/21 Topics:
  • Career Advice
  • Reading Research Papers
Optional Reading
Completed modules:
  • C5M1: Recurrent Neural Networks (slides)
Quizzes (due at 8:30am):
  • Recurrent Neural Networks
Programming Assignments (due at 8:30am):
  • Building a Recurrent Neural Network - Step by Step
  • Dinosaur Land -- Character-level Language Modeling
  • Jazz improvisation with LSTM
Lecture 9 05/28 Topics:
  • Deep Reinforcement Learning
  • Slides

Optional Reading:
Completed modules:
  • C5M2: Natural Language Processing and Word Embeddings (slides)
  • C5M3: Sequence-to-Sequence Models (slides)
Quizzes (due at 8:30am):
  • Natural Language Processing and Word Embeddings
  • Sequence Models and Attention Mechanism
Programming Assignments (due at 8:30am):
  • Operations on Word Vectors - Debiasing
  • Emojify!
  • Neural Machine Translation with Attention
  • Trigger Word Detection
Lecture 10 06/04 Topics:
  • Class wrap-up
  • What's next?
  • Slides
Final Poster and Project Report Due 06/09
11:59pm
Instructions for Poster and Project Report Note: Late days cannot be applied to the final poster and report.
Poster Session 06/10
Monday
Poster Session
  • Date: June 10, Monday
  • Time: 8:30am - 11:30am
  • Location: Alumni Center