Machine Learning is related to other major fields such as Artificial Intelligence, Data Science, Data Mining, Pattern Recognition, and Statistics.
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on enabling computers to learn automatically from data without being explicitly programmed.
Machine Learning (ML) is an interdisciplinary field that combines ideas from various domains like Artificial Intelligence (AI), Data Science, Data Mining, Pattern Recognition, and Statistics. These fields contribute different tools and perspectives, making ML a powerful and adaptive technology.
1. Relationship between Machine Learning and Artificial Intelligence (AI)
Machine Learning is a subfield of AI.
AI is a broader area that focuses on building intelligent agents like robots or software systems that simulate human-like intelligence.
Early AI systems relied on logic-based reasoning, which struggled in complex or real-time environments, leading to AI winters (periods of low progress).
ML revived AI by introducing data-driven learning methods that allow systems to learn patterns from data and make predictions.
Deep Learning is a subbranch of ML that uses neural networks modeled after the human brain to handle complex tasks like image and speech recognition.
Figure 1.3: AI ⟶ Machine Learning ⟶ Deep Learning
2. Relationship with Data Science, Data Mining, and Big Data
a) Data Science
ML is a branch of Data Science.
Data Science is an umbrella field that includes data collection, processing, and analysis.
ML is the core technique used in Data Science for building models and making predictions.
b) Big Data
A subarea of Data Science dealing with large-scale data, characterized by:
Volume: Massive data (e.g., from Facebook, Twitter)
Variety: Different data formats like images, videos, text
Velocity: Fast generation and processing speed
Big Data fuels ML models, especially deep learning, with massive training datasets.
c) Data Mining
Data Mining is often considered a sister field to ML.
Focus: Extracting hidden patterns or relationships in data.
Difference:
Data Mining = Pattern extraction
Machine Learning = Prediction using those patterns
d) Data Analytics
Data Analytics is about analyzing data to extract useful insights.
Predictive Analytics, a type of data analytics, shares almost all algorithms with ML.
e) Pattern Recognition
Pattern Recognition focuses on identifying patterns and features in data.
It is a direct application of ML techniques like classification and clustering.
Figure 1.4: Shows the interrelationship among ML, Data Science, Big Data, Analytics, etc.
3. Relationship with Statistics
Statistics and ML both use data, but their approaches differ.
Statistics:
Begins with a hypothesis and tests it using statistical methods.
Involves complex formulas, assumptions, and requires expert statisticians.
Emphasizes interpretation, validation, and mathematical rigor.
Machine Learning:
Focuses more on prediction than explanation.
Uses less mathematical assumptions.
Relies on automated tools for learning from data.
Some scholars even refer to ML as the modern evolution of “Old Statistics”.
