Lecture Notes on AI and ML for Geodata Analysis

Introduction

• Second session of the course on AI and ML for Geodata Analysis.
• Participants are encouraged to watch sessions on YouTube.
• Registered participants can take quizzes on ISRO LMS for certification.
• Emphasized the importance of cooperation for a fruitful learning experience.

Importance of Data

• Quote by Professor GTH James: "For the 21st century, data is the sword, and whoever can handle data properly will be called a samurai."
• The era of big data demands advanced algorithms.

What is Machine Learning?

• Subset of Artificial Intelligence (AI).
• Automates tasks by learning from data without explicit programming for each task.
• Algorithms learn relationships between input and output datasets.

Deep Learning

• Further subset of machine learning using deep neural networks.
• Utilizes multiple layers for learning from datasets.
• Model depth indicates the number of layers, which could lead to better models, but not always.

Differences Between Machine Learning and Deep Learning

• Problem-Solving Approach:
  • Machine Learning: Manual feature extraction.
  • Deep Learning: Minimal human intervention in feature extraction.
• Training Methods:
  • Machine Learning: Supervised, unsupervised, reinforcement, etc.
  • Deep Learning: Autoencoders, CNNs, RNNs, etc.
• Complexity of Algorithms:
  • Diverse algorithms in ML; complex architecture in Deep Learning.
• Data Interpretation:
  • ML requires structured data; Deep Learning can handle unstructured data.
• Infrastructure Needs:
  • ML can run on single instances; Deep Learning requires large storage and computational power.

Key Concepts of Machine Learning

• Machine Learning Definition:
  • A method for automating data analysis.
  • Collects multiple examples to train the system.
• Types of Machine Learning Algorithms:
  • Supervised Learning: Uses labeled data for training.
  • Unsupervised Learning: Discover patterns in unlabeled data.
  • Reinforcement Learning: System learns based on feedback (reward/penalty).

Machine Learning Problems in Geodata Analysis

• Classification: Assigns finite values based on input (e.g., land use patterns).
• Regression: Outputs continuous values (numerical weather modeling).
• Clustering: Groups similar data points (unsupervised learning).

Supervised Learning

• Training Process:
  • Data split into training, validation, and testing sets.
• Example of Classification:
  • Training using representative pixels for different land use categories.
• Disadvantages:
  • Reliant on labeled data which can be costly and time-consuming to obtain.

Common Supervised Learning Algorithms

• Paralle Classification Algorithm:
  • Classifies based on statistical calculations of means and standard deviations.
• Minimum Distance to Means Classification:
  • Classifies based on the closest mean.
• Mahalanobis Decision Rule:
  • Considers the covariance of classes to improve classification accuracy.
• Maximum Likelihood Classification:
  • Uses statistical distribution for classification; highly accurate but computationally intensive.

Decision Trees and Random Forests

• Decision Trees:
  • Series of binary decisions for classification.
• Random Forest Classifier:
  • Ensemble method using multiple trees to improve accuracy and reduce overfitting.

Support Vector Machines (SVM)

• Classification and Regression Tasks:
  • Finds optimal hyperplane to separate classes in high-dimensional space.
• Advantages:
  • Effective in high-dimensional spaces; memory efficient.

Unsupervised Learning

• Clustering:
  • Groups similar data points without labeled training data.
• K-Means Clustering:
  • Assigns data points to clusters based on distance from a centroid.

Summary of Learning Types

• Supervised Learning:
  • Requires labeled data; high accuracy and simpler.
• Unsupervised Learning:
  • No labeled data; less accurate but can discover natural patterns.
• Semi-Supervised:
  • Combines small labeled data with a larger unlabeled dataset.
• Reinforcement Learning:
  • Learns through trial and error and feedback.

Conclusion

• The session concludes with a Q&A.
• Participants encouraged to take a short break before reconvening.