Stanford CS231n Core Topics Overview | Generated by AI
Question: What is the course syllabus for Stanford CS231n by Andrej Karpathy?
Answer:
Stanford CS231n, titled “Convolutional Neural Networks for Visual Recognition” (now often called “Deep Learning for Computer Vision”), was originally designed and primarily taught by Andrej Karpathy during its early offerings (Winter 2015 and Winter 2016 quarters). He created the foundational lecture notes, assignments, and much of the course structure that is still widely used today.
The classic syllabus from the Andrej Karpathy era (Winter 2016) covers the following main topics across approximately 20 lectures:
- Introduction to Computer Vision and course overview
- Image Classification (data-driven approach, k-Nearest Neighbor)
- Linear Classification (score functions, loss functions like SVM and Softmax)
- Optimization (gradient descent, stochastic gradient descent, backpropagation)
- Neural Networks (fully connected networks, activation functions, architectures)
- Training Neural Networks (initialization, regularization, dropout, batch normalization, data augmentation)
- Convolutional Neural Networks (ConvNets) (convolution, pooling, architectures like AlexNet, VGG, GoogLeNet, ResNet)
- Understanding and Visualizing ConvNets (saliency maps, gradients, deconvolutions)
- Object Detection (sliding window, region proposals, R-CNN family)
- Localization and Segmentation (semantic segmentation, instance segmentation)
- Recurrent Neural Networks (RNNs) and applications like image captioning
- Advanced topics (transfer learning, efficient convolutions, modern practices)
The course includes hands-on assignments implementing these concepts (e.g., kNN, fully-connected nets, ConvNets in frameworks like PyTorch/TensorFlow, network visualization) and a final project.
Note that the current version of CS231n (e.g., Spring 2025) has evolved under instructors like Fei-Fei Li and others, incorporating newer topics such as generative models, transformers in vision (ViTs), and human-centered AI, while retaining the core structure from Karpathy’s original design.
The original lecture notes from the Karpathy era remain publicly available and are still the most referenced version of the course materials.
References:
- Original CS231n Course Website (current, with links to past offerings)
- CS231n Lecture Notes (classic version by Andrej Karpathy)
- Winter 2016 Syllabus and Slides
- Andrej Karpathy’s Academic Page mentioning CS231n