Instructor: Saining Xie
Location: CIWW 312
Syllabus (TBD; The following can be potential topics.)
Neural networks and deep learning. Large-scale Optimization. Language modeling: N-gram models, word embeddings, LSTM, transformers, BERT, GPT. Vision modeling: convnets and vision transformers. Classification and detection (Mask R-CNN, DETR), segmentation (FCN, SAM), motion, depth. Vision self-supervised learning. Generative models (GANs, VAEs, Diffusions). Alignment (RLHF). Multi-modal learning and language and vision models (CLIP, BLIP, Flamingo).
Note: Please be aware that this course is not designed as an introductory AI/machine learning class. Rather, it is intended to serve as an advanced graduate seminar with a strong emphasis on research. Participants in this course should already possess a solid foundation in deep learning and computer vision, as this background is necessary for active engagement in class discussions and successful completion of the final project.
**Students are expected to have completed at least one of these courses: 1) Deep Learning, 2) Machine Learning, or 3) Computer Vision.**
- Python programming; Deep learning programming with PyTorch or JAX
- Foundations of machine learning
- Foundations of deep learning
- Linear algebra
- Probability and statistics
The course will encompass a combination of lectures, paper reading seminars, and semester-long hands-on projects. Students will be organized into groups of 4-5, working collaboratively on projects and engaging in presentations and discussions during paper seminars.