Week 1-2: Introduction to Computer Vision and Machine Learning
Overview of computer vision and machine learning applications
Supervised, unsupervised, and semi-supervised learning
Linear regression, logistic regression, and optimization techniques
Week 3-4: Image Classification and Object Detection
Introduction to image classification and object detection
Feature extraction methods (e.g., SIFT, HOG)
Classification algorithms (e.g., k-NN, SVM, CNN)
Week 5-6: Deep Learning for Computer Vision
Deep learning architectures (e.g., CNN, RNN, autoencoder)
Backpropagation algorithm and optimization methods
Hands-on exercises on building and training deep learning models in TensorFlow or PyTorch
Week 7-8: Transfer Learning and Fine-tuning
Transfer learning and fine-tuning of pre-trained models
Applications of transfer learning in computer vision
Hands-on exercises on using pre-trained models in TensorFlow or PyTorch
Week 9-10: Object Tracking and Segmentation
Introduction to object tracking and segmentation
Methods for object tracking and segmentation
Applications of object tracking and segmentation in computer vision
Week 11-12: Advanced Topics and Project Work
Advanced topics in machine learning for computer vision (e.g., generative models, attention mechanisms)
Group or individual projects to apply machine learning techniques to a real-world dataset in computer vision