GIS

1. MACHINE LEARNING
APPROACHES

2. WHAT IS MACHINE
LEARNING?
• Machine learning enables systems to
learn patterns from data and
improve performance over time
without being explicitly
programmed.
• It involves the development of
algorithms that learn from
experience and adapt to new inputs.

3. TYPE OF
MACHINE
LEARNING
APPROACHES
• Supervised Learning
• Unsupervised Learning
• Semi-Supervised
Learning
• Reinforcement Learning
• Deep Learning

4. SUPERVISED LEARNING APPROACHES
• The model is trained on labelled data.
• The algorithm receives an input dataset along with the correct outputs,
and the goal is to learn a general rule that maps inputs to outputs.
• Examples : Linear regression, Logistic regression, Support Vector Machine
(SVM), Decision Trees and Random Forests, K-Nearest Neighbors (KNN)

5. UNSUPERVISED LEARNING APPROACHES
• Model works on data without labels.
• The system tries to learn the patterns and the structure from the data without any
reference to known or labeled outcomes.
• Examples : Clustering, Dimensionality Reduction

6. SEMI-SUPERVISED
LEARNING APPROACHES
• This approach falls between supervised and
unsupervised learning.
• Uses labelled and unlabeled data for training –
typically a small amount of labelled data with a
large amount of unlabeled data.

7. REINFORCEMENT
LEARNING APPROACHES
• A type of machine learning where an agent learns to make decisions by
interacting with an environment.
• The agent takes actions and receives feedback in the form of rewards or
penalties, guiding it to learn the optimal strategy to achieve a specific
goal.
• Through trial and error, the agent improves its decision-making over
time, ultimately maximizing its cumulative reward.
• This is commonly used in applications such as game playing, robotics,
and autonomous systems.

8. DEEP LEARNING
APPROACHES
• A subset of machine learning that uses neural
networks with many layers to analyze various
factors with a complex structure.
• This is perfect for data that has hierarchical or
spatial structures, like images and sound.
• Examples : Convolutional Neural Networks
(CNNs), Recurrent Neural Networks (RNNs),
Long Short-Term Memory Networks (LSTMs),
Autoencoders

9. APPLICATIONS OF MACHINE LEARNING
APPROACHES
Healthcare: Disease
diagnosis
Finance: Fraud
detection
Manufacturing: Quality control
Transportation: Traffic
prediction

10. CHALLENGES AND CONSIDERATIONS
• Data quality
• Overfitting or underfitting
• Interpretability

11. THANK
YOU!
Group Members
Name Index
L.S Hemage 216046F
MISHARA W.A.P.C. 216080D
P.A.U.S Rupasinghe 216113J
D.N Wijendra 216153F