By the end of this experiment, participants will be able to:

1. Understand the Viterbi algorithm as a dynamic programming solution for finding the most likely sequence of hidden states in Hidden Markov Models.

2. Comprehend the mathematical foundations of Viterbi decoding, including the recursive computation of maximum probabilities and backtracking for optimal path recovery.

3. Interpret emission and transition matrices derived from training corpora and understand their role in statistical POS tagging.

4. Apply the Viterbi algorithm step-by-step to decode POS tag sequences for test sentences using pre-computed probability matrices.

5. Fill Viterbi tables systematically by computing maximum probabilities for each word-tag combination and maintaining backpointers for path reconstruction.

6. Analyze the decoding process by understanding how the algorithm balances emission probabilities (word-tag likelihood) with transition probabilities (tag sequence likelihood).

7. Compare different corpus examples to observe how training data characteristics affect the resulting probability distributions and decoding outcomes.

8. Evaluate the efficiency of dynamic programming in solving the exponential search space problem of finding optimal tag sequences.

9. Understand practical applications of the Viterbi algorithm beyond POS tagging, including speech recognition, bioinformatics, and other sequence labeling tasks.

10. Practice interactive problem-solving through hands-on simulation exercises that reinforce theoretical understanding with practical implementation.