In the latest installment of the Complete Artificial Intelligence Course 2026, we explore the Hidden Markov Model (HMM), a powerful statistical tool that extends the simple Markov Model to handle uncertainty in real-world predictions.
Unlike standard Markov chains, HMMs incorporate hidden states—variables that are not directly observable but influence the data we can see. This makes them indispensable for tasks like speech recognition, bioinformatics, and financial modeling, where the underlying process must be inferred from noisy observations.
The video breaks down the three fundamental problems HMMs solve:
- Evaluation: Computing the probability of a sequence of observations.
- Decoding: Finding the most likely sequence of hidden states given observations.
- Learning: Estimating model parameters from data.
Using intuitive diagrams and step-by-step explanations, the tutorial demonstrates how the Forward-Backward algorithm and the Viterbi algorithm work in practice. These algorithms form the backbone of many modern AI systems, enabling machines to predict outcomes even when not all information is visible.
Whether you are a student of machine learning or a seasoned developer, understanding HMMs is crucial for building robust predictive models. This lecture provides a clear, hands-on guide to mastering this essential concept.