Probabilistic AI typically aims to teach the foundations of artificial intelligence that involve uncertainty, probability theory, and statistical methods. The learning objectives for such a course might include:

1. Understanding Probability Theory:

• Grasp basic concepts of probability, including random variables, distributions, and conditional probabilities.
• Learn how to apply these concepts to model uncertainty in AI systems.

1. Bayesian Inference:

• Learn the principles of Bayesian methods for updating beliefs and making predictions from data.
• Understand Bayesian networks, which represent probabilistic dependencies among variables.

1. Statistical Learning:

• Understand statistical learning techniques, including regression, classification, and clustering, under uncertainty.
• Learn how to use probabilistic models for supervised and unsupervised learning.

1. Markov Chains and Monte Carlo Methods:

• Understand Markov processes and how they are used in probabilistic AI models.
• Learn Monte Carlo methods (e.g., Markov Chain Monte Carlo) for sampling and approximating complex probabilistic distributions.

1. Hidden Markov Models and Dynamic Systems:

• Learn to model sequential data using Hidden Markov Models (HMMs) and their applications in AI, such as speech recognition or bioinformatics.

1. Probabilistic Programming and Tools:

• Gain familiarity with tools and libraries used for probabilistic modeling, such as PyMC3, Stan, or TensorFlow Probability.

1. Decision Making under Uncertainty:

• Understand how to make decisions when outcomes are uncertain, focusing on techniques such as reinforcement learning and decision theory.

1. Evaluation and Model Comparison:

• Learn to assess the performance of probabilistic models using metrics like likelihood, posterior predictive checks, and cross-validation.

1. Applications of Probabilistic AI:

• Explore real-world applications, including robotics, natural language processing, computer vision, and medical diagnostics, where probabilistic AI techniques are applied.

By the end of the course, students are expected to be capable of designing, implementing, and evaluating AI models that handle uncertainty and make probabilistic predictions.

Probabilistic AI typically aims to teach the foundations of artificial intelligence that involve uncertainty, probability theory, and statistical methods. The learning objectives for such a course might include:

1. Understanding Probability Theory:

• Grasp basic concepts of probability, including random variables, distributions, and conditional probabilities.
• Learn how to apply these concepts to model uncertainty in AI systems.

1. Bayesian Inference:

• Learn the principles of Bayesian methods for updating beliefs and making predictions from data.
• Understand Bayesian networks, which represent probabilistic dependencies among variables.

1. Statistical Learning:

• Understand statistical learning techniques, including regression, classification, and clustering, under uncertainty.
• Learn how to use probabilistic models for supervised and unsupervised learning.

1. Markov Chains and Monte Carlo Methods:

• Understand Markov processes and how they are used in probabilistic AI models.
• Learn Monte Carlo methods (e.g., Markov Chain Monte Carlo) for sampling and approximating complex probabilistic distributions.

1. Hidden Markov Models and Dynamic Systems:

• Learn to model sequential data using Hidden Markov Models (HMMs) and their applications in AI, such as speech recognition or bioinformatics.

1. Probabilistic Programming and Tools:

• Gain familiarity with tools and libraries used for probabilistic modeling, such as PyMC3, Stan, or TensorFlow Probability.

1. Decision Making under Uncertainty:

• Understand how to make decisions when outcomes are uncertain, focusing on techniques such as reinforcement learning and decision theory.

1. Evaluation and Model Comparison:

• Learn to assess the performance of probabilistic models using metrics like likelihood, posterior predictive checks, and cross-validation.

1. Applications of Probabilistic AI:

• Explore real-world applications, including robotics, natural language processing, computer vision, and medical diagnostics, where probabilistic AI techniques are applied.

By the end of the course, students are expected to be capable of designing, implementing, and evaluating AI models that handle uncertainty and make probabilistic predictions.