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Key Concepts in Machine Learning: Foundations and Techniques

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Course
COMPUTER SCIENCE

Institution
Harvard University

This document covers the key concepts in machine learning, including supervised and unsupervised learning, model training, and evaluation metrics. It explains crucial concepts like overfitting, underfitting, and feature engineering, alongside techniques such as cross-validation to improve model acc...

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Uploaded on January 31, 2025
Number of pages 6
Written in 2024/2025
Type Other
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machine learning
cs1004
supervised learning
unsupervised learning
model training
overfitting
underfitting
feature engineering
cross validation
evaluation metrics
classification
regression
clustering

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Machine Learning & AI Complete Exam Study Pack (24 Documents)

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Key Concepts in Machine Learning
Machine Learning (ML) is a rapidly growing field of artificial intelligence that
allows systems to learn from data, identify patterns, and make decisions with
minimal human intervention. Understanding the fundamental concepts of ML is
essential for anyone who wants to explore the field further. Below are some of
the core concepts that form the foundation of machine learning.

1. Supervised Learning
Supervised learning is one of the most common types of machine learning, where
the model is trained on a labeled dataset. In this approach, the algorithm learns
from the input data along with the correct output (label) so that it can predict the
output for new, unseen data.

 Training Data: The dataset used to train the model contains input-output
pairs. The algorithm adjusts its internal parameters to minimize the error
between predicted outputs and actual outputs.
 Examples: Common supervised learning tasks include classification (e.g.,
identifying whether an email is spam or not) and regression (e.g., predicting
house prices based on various features like location, size, etc.).
 Algorithms Used: Some popular supervised learning algorithms include
Linear Regression, Decision Trees, Support Vector Machines (SVM), and
Neural Networks.

2. Unsupervised Learning
In contrast to supervised learning, unsupervised learning involves training a
model on data that is not labeled. The goal is to identify hidden patterns or
structures in the data without the need for predefined output labels.

 Clustering: One of the primary tasks in unsupervised learning is clustering,
where the algorithm groups similar data points together. For example,

, customer segmentation in marketing is a clustering problem where
customers are grouped based on purchasing behavior.
 Dimensionality Reduction: This is another important unsupervised task
where the algorithm reduces the number of features (dimensions) in the
data while retaining as much information as possible. Principal Component
Analysis (PCA) is a popular technique for this purpose.
 Algorithms Used: Common unsupervised learning algorithms include K-
Means Clustering, DBSCAN (Density-Based Spatial Clustering of Applications
with Noise), and Hierarchical Clustering.

3. Semi-Supervised Learning
Semi-supervised learning lies between supervised and unsupervised learning. In
this approach, the model is trained using a small amount of labeled data and a
large amount of unlabeled data. The algorithm attempts to make use of both
labeled and unlabeled data to improve the model's accuracy.

 Labeled vs. Unlabeled Data: Semi-supervised learning leverages the fact
that acquiring labeled data can be expensive or time-consuming, but
unlabeled data is often abundant. The model uses the labeled data for
guidance but tries to infer structure from the unlabeled data.
 Applications: Semi-supervised learning is used in scenarios where labeling
large datasets is impractical, such as in medical image analysis, speech
recognition, and web content classification.
 Algorithms Used: Some algorithms used in semi-supervised learning
include Semi-Supervised Support Vector Machines (S3VM) and Self-
training.

4. Reinforcement Learning
Reinforcement learning (RL) is a type of machine learning where an agent learns
how to make decisions by interacting with its environment. In this paradigm, the
agent takes actions and receives feedback in the form of rewards or penalties.

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