Projects carried out during the course Artificial Intelligence in Kungliga Tekniska Högskolan (KTH).

HMM0_Next_Emission_Distribution: The objective of this task is to predict how the system will evolve over time and estimate the probability for different emissions / events in the system i.e. what can be observed from the HMM.

HMM1_Probability_of_Emission_Sequence: The objective of this task is to calculate the probability to observe a certain emission sequence given a HMM model.

HMM2_Estimate_Sequence_of_States: The objective of this task is to calculate the most likely sequence of (hidden) states that the system moves through given an emission sequence and an HMM model.

HMM3_Estimate_Model : The objective of this task is to estimate the model parameters for an HMM given a starting guess of a HMM (transition matrix, emission matrix and initial state probability distribution) and a sequence of emissions. The HMM maximizes the probability of observing the given sequence of emissions.

Tic_Tac_Toe_2D : Implementation of a program that plays the game of Tic-Tac-Toe. The program wins (or not lose) as often as possible, finding the best possible move for player X given a particular state of the game. The board is a 2D one.

Tic_Tac_Toe_3D: Implementation of a program that plays the game of Tic-Tac-Toe. The program wins (or not lose) as often as possible, finding the best possible move for player X given a particular state of the game. The board is a 3D one.