Course 1
Introduction to Data Science and Machine Learning for Images
This foundational course provides an introduction to the key concepts of data science, machine learning, and image analysis. Students will learn the basics of handling image data, applying traditional machine learning techniques, and building their first image classification models.
Week 1
Introduction to Data Science and Image Data
- Overview of image data types and formats.
- The basics of image processing: resizing, normalization, and transformations.
Week 2
Preprocessing Images for Machine Learning
- Techniques for data augmentation and image preprocessing.
- Working with image datasets: loading and managing large datasets.
Week 3
Classical Machine Learning Algorithms for Image Classification
- KNN, SVM, and decision trees for image classification.
- Feature extraction techniques: HOG, SIFT, and edge detection.
Week 4
Evaluation and Model Optimization
- Model evaluation using cross-validation, confusion matrix, and ROC curves.
- Hyperparameter tuning for machine learning models.
Week 5
Introduction to Convolutional Neural Networks (CNNs)
- Fundamentals of CNN architecture: layers, filters, and activations.
- Implementing simple CNNs for image classification.
Week 6
Final Project: Building an Image Classifier
Work with a dataset (e.g., CIFAR-10, MNIST) to build and evaluate a CNN for image classification.