Explore how intelligent agents take actions under uncertainty, moving from deterministic environments to non-deterministic and partially observable ones, with real-world examples like elections and climate policy.

Learn how probability quantifies uncertainty and guides decision making. Explore degree of belief, prior and posterior probabilities, sample space, mutually exclusive and exhaustive events, and joint distributions.

Explore independence and conditional independence, learn conditional probability and the rule of multiplication, and see how conditional independence reduces joint distribution size in Bayes theorem and Bayesian networks.

Explore the Anaconda Navigator interface, launch Jupyter Notebook for Python learning, and note Spyder alongside optional tools Louis, Orange Tree, and R studio, plus environment for multiple navigator versions.

Learn two methods to open Jupyter Notebook: via the Anaconda Navigator launch button and via the Start Menu, with the Notebook home page appearing in your browser.

Learn the Jupyter notebook layout, rename notebooks, navigate the menu and toolbar, switch between edit and command modes, and work with code and markdown cells, including markdown headers and shortcuts.

Learn essential Jupyter notebook shortcuts for running cells with shift enter, switching between code and markdown with M and Y, and deleting or adding cells.

Explore Bayes' theorem as a tool for reasoning under uncertainty, detailing conditional probability, prior probability, and likelihood. See how to diagnose causes and update beliefs with new evidence.

Implement a Bayesian network in Python using the Pomegranate library to model cancer risk from smoking and asbestos, with x-ray and blood vomiting as outcomes and perform inference with evidence.

Explore how time introduces uncertainty by using time slices, transition and sensor models to predict future states, and apply the Markov property to justify relying on the previous time step.

Explore hidden Markov models by combining hidden states, sensor observations, and the Markov property to estimate future states; learn the state, transition, observation, and initial models in robotics and sensing.

build a hidden Markov model in Python with pomegranate library to infer the sequence of sunny and rainy days from observations, defining states, observation model, transition model, and initial state.

Explore how to quantify uncertainty using probability, conditional independence, and Bayes theorem, then build models like Bayesian networks and Markov chains for time and uncertainty.