Probability & Statistics for AI

Name: Probability & Statistics for AI
Availability: InStock
Author: FreeAcademy

Intermediate

Assumes foundational knowledge. Builds on basics.

Master the probabilistic foundations of artificial intelligence. Learn probability fundamentals, Bayes' theorem, distributions, expected value, maximum likelihood estimation, and how AI systems handle uncertainty to make predictions.

Contenido del Curso

6 módulos • 20 lecciones

Probability Fundamentals

4 lecciones

1.1

Independence and Joint Probability

Bayes' Theorem

3 lecciones

2.1

Introduction to Bayes' Theorem

2.2

Updating Beliefs with Evidence

2.3

Bayes' Theorem in AI Applications

Probability Distributions

3 lecciones

3.1

Discrete Probability Distributions

3.2

Continuous Distributions and the Normal Distribution

3.3

Softmax and Temperature in AI

Expected Value and Variance

3 lecciones

4.1

Expected Value: Predicting Average Outcomes

4.2

Variance and Standard Deviation

4.3

Measuring Uncertainty in AI Predictions

Maximum Likelihood Estimation

3 lecciones

5.1

What is Maximum Likelihood Estimation?

5.2

MLE in Practice: Training AI Models

5.3

Loss Functions and Optimization

Sampling, Randomness, and Evaluation

4 lecciones

6.1

Sampling Methods in AI

6.2

How LLMs Pick Tokens

6.3

Evaluation Metrics: Precision, Recall, and More

6.4

Understanding Confusion Matrices

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Probability & Statistics for AI

Contenido del Curso

Probability Fundamentals

What is Probability?

Events and Sample Spaces

Conditional Probability

Independence and Joint Probability

Bayes' Theorem

Introduction to Bayes' Theorem

Updating Beliefs with Evidence

Bayes' Theorem in AI Applications

Probability Distributions

Discrete Probability Distributions

Continuous Distributions and the Normal Distribution

Softmax and Temperature in AI

Expected Value and Variance

Expected Value: Predicting Average Outcomes

Variance and Standard Deviation

Measuring Uncertainty in AI Predictions

Maximum Likelihood Estimation

What is Maximum Likelihood Estimation?

MLE in Practice: Training AI Models

Loss Functions and Optimization

Sampling, Randomness, and Evaluation

Sampling Methods in AI

How LLMs Pick Tokens

Evaluation Metrics: Precision, Recall, and More

Understanding Confusion Matrices

Reseñas

Probability & Statistics for AI

Contenido del Curso

Probability Fundamentals

What is Probability?

Events and Sample Spaces

Conditional Probability

Independence and Joint Probability

Bayes' Theorem

Introduction to Bayes' Theorem

Updating Beliefs with Evidence

Bayes' Theorem in AI Applications

Probability Distributions

Discrete Probability Distributions

Continuous Distributions and the Normal Distribution

Softmax and Temperature in AI

Expected Value and Variance

Expected Value: Predicting Average Outcomes

Variance and Standard Deviation

Measuring Uncertainty in AI Predictions

Maximum Likelihood Estimation

What is Maximum Likelihood Estimation?

MLE in Practice: Training AI Models

Loss Functions and Optimization

Sampling, Randomness, and Evaluation

Sampling Methods in AI

How LLMs Pick Tokens

Evaluation Metrics: Precision, Recall, and More

Understanding Confusion Matrices

Reseñas