Module 8
What is Machine Learning (ML)?
Machine Learning is a branch of Artificial Intelligence that enables computers to learn patterns from data and make predictions or decisions without being explicitly programmed.
What are the main types of Machine Learning?
The three main types are:
Supervised Learning – learning from labeled data
Unsupervised Learning – learning from unlabeled data
Reinforcement Learning – learning through rewards and penalties
Compare Supervised and Unsupervised Machine Learning
Supervised and unsupervised learning are two major categories of machine learning based on the availability of labeled data.
Supervised Machine Learning uses labeled datasets, where each input is associated with a known output. The model learns a mapping between inputs and outputs and is mainly used for classification and regression problems. Common algorithms include Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines. Model performance can be easily evaluated using metrics such as accuracy, precision, recall, and RMSE.
Unsupervised Machine Learning, on the other hand, works with unlabeled data. The objective is to identify hidden patterns, structures, or relationships within the data. It is commonly used for clustering and dimensionality reduction tasks. Popular algorithms include K-Means Clustering, Hierarchical Clustering, DBSCAN, and Principal Component Analysis (PCA). Evaluating results is more challenging due to the absence of known outputs.
List out algorithms of Supervised ML
The commonly used supervised machine learning algorithms are:
Linear Regression/Multiple Linear Regression/Logistic Regression
Support Vector Machine (SVM)
Decision TreeRandom Forest
k-Nearest Neighbors (k-NN)
Naïve Bayes Classifier
Gradient Boosting (GBM)/ XGBoost
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)(supervised deep learning)
List out algorithms of Unsupervised ML
Commonly used unsupervised machine learning algorithms include:
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Gaussian Mixture Models (GMM)
- Principal Component Analysis (PCA)/ Independent Component Analysis (ICA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- UMAP (Uniform Manifold Approximation and Projection)
What are the ML Frameworks & Libraries?
TensorFlow and TensorFlow Lite are widely used for developing, training, and deploying machine learning models, with TensorFlow Lite specifically optimized for lightweight and embedded systems.
PyTorch is primarily used for machine learning research and experimentation due to its dynamic computation graph and ease of model development.
OpenCV is an open-source computer vision library used for image and video processing tasks such as object detection, feature extraction, and image enhancement.
ONNX Runtime enables cross-platform machine learning inference by allowing models to be executed efficiently across different hardware and software environments.
Scikit-learn is a popular Python library for implementing classical machine learning algorithms such as classification, regression, clustering, and dimensionality reduction
What are the key application of ML in UAV Engineering?
Commonly used applications include:
- Object-detection and tracking
- Obstacle avoidance
- Navigation and path prediction
- Fault detection and health monitoring
- Precise Agriculture
- Terrian Mapping
- Anomaly detection
- Swarm Drone coordination