Computer Vision

More information here

Computer Vision and Image Processing

• Projects & Code: Here
• Course Notes: Here
• Topics:
  • Digital Images, videos and OpenCV
  • Kernals, filters and convolution
  • Image rotation, padding, segmentation, edge detection
  • Gaussian Blur, Histogram Equalization
  • Image Classification
  • K-Nearest Neighbours (KNN)
  • Linear Classifiers
  • Test / Validation / Train split
  • Neural Networks
  • Convolutional Neural Networks
  • Mini-Batch Gradient Descent
  • Sigmoid, Softmax
  • Data Augmentation
  • Deploying models on IBM Cloud
  • Object Detection, Haar Classifiers
  • R-CNN

Additional Resources

1. Introduction to Machine Learning by Stanford University
2. Pattern Recognition and Machine Learning by Bishop
3. The Elements of Statistical Learning (ESL) by Hastie, Tibshirani, and Friedman
4. Information Theory, Inference, and Learning Algorithms by MacKay
5. Bayesian Reasoning and Machine Learning by Barber
6. Reinforcement Learning: An Introduction by Sutton and Barto
7. Math for ML by Deisenroth, Faisal, and Ong
8. Understanding Machine Learning: From Theory to Algorithms by Shalev-Shwartz and Ben-David
9. Machine Learning: a Probabilistic Perspective by Murphy