MLG 014 Shallow Algos 3

Machine Learning Guide

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8y ago 48:27

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Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/14

Anomaly Detection Systems

Applications: Credit card fraud detection and server activity monitoring.
Concept: Identifying outliers on a bell curve.
Statistics: Central role of the Gaussian distribution (normal distribution) in detecting anomalies.
Process: Identifying significant deviations from the mean to detect outliers.

Recommender Systems

Types:
- Content Filtering: Uses features of items (e.g., Pandora’s Music Genome Project).
- Collaborative Filtering: Based on user behavior and preferences, like "Users Also Liked" model utilized in platforms like Netflix and Amazon.
Applications in Machine Learning: Linear regression applications in recommender systems for predicting user preferences.

Markov Chains

Explanation: Series of states with probabilities dictating transitions to next states; present state is sufficient for predicting next state (Markov principle).
Use Cases: Often found in reinforcement learning and operations research.
Monte Carlo Simulation: Running simulations to determine the expected value or probable outcomes of Markov processes.

Resource

Andrew NG's Coursera Course - Week 9: Focuses on anomaly detection and recommender systems.

57 episodes

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Machining #Learn

MLG 014 Shallow Algos 3

Machine Learning Guide

591 subscribers

published 8y ago

MP3•Episode home

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/14

Anomaly Detection Systems

Applications: Credit card fraud detection and server activity monitoring.
Concept: Identifying outliers on a bell curve.
Statistics: Central role of the Gaussian distribution (normal distribution) in detecting anomalies.
Process: Identifying significant deviations from the mean to detect outliers.

Recommender Systems

Types:
- Content Filtering: Uses features of items (e.g., Pandora’s Music Genome Project).
- Collaborative Filtering: Based on user behavior and preferences, like "Users Also Liked" model utilized in platforms like Netflix and Amazon.
Applications in Machine Learning: Linear regression applications in recommender systems for predicting user preferences.

Markov Chains

Explanation: Series of states with probabilities dictating transitions to next states; present state is sufficient for predicting next state (Markov principle).
Use Cases: Often found in reinforcement learning and operations research.
Monte Carlo Simulation: Running simulations to determine the expected value or probable outcomes of Markov processes.

Resource

Andrew NG's Coursera Course - Week 9: Focuses on anomaly detection and recommender systems.

57 episodes

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Machining #Learn

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