The code for the given repo is private.

The private repo contains implementation of different AI algorithms such as

1. SEARCH:

   Problem Statement: calculate a route between two points in Romania while seeking to minimize time and space cost. Algorithms:

   1. Implemented Priority Queue to order search frontier.
   2. Implemented Breadth first search on the graph. It returns a path of nodes from a given start node to a given end node, as a list.
   3. Impelmented uniform-cost search, using PriorityQueue as the frontier. It return a path from the start to the goal node as a list of nodes.
   4. Implemented A-star search using Euclidean distance as heuristic.
   5. Implemented bidirectional uniform-cost search. This requires starting the search at both the start and goal nodes.
   6. Implemented bidirectional A-star search. Here we calculate a heuristic for both the start-to-goal search and the goal-to-start search. Optimal path calculated is the same path found via unidirectional UCS and A*.
   7. Implemented tridirectional search using UCS. Starting from each goal node, performed a uniform-cost search and keep on expanding until two of the three searches meet.

2. GAME PLAY: Problem statement: AI that can play and win a game of Castle Isolation. Algorithm:

   1. Implemented Minimax algorithm. Defeated a Random Player >=90% of the time.
   2. Implemnted Alpha Beta pruning. Minimax level 2 >= 70% of the time.

3. BAYES NETWORK: Problem Statement: Build a Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables.

   1. Setting the probabilities, performing inference, sampling and finally building the network. Perfomed Posterior distribution.
   2. Implemented Gibbs sampling and Metropolis-Hastings sampling to idenitfy which works better.

4. DECISION TREE: Algorithms:

   1. Implemnted Decision tree from scratch.
   2. Implemented Random forest from scratch.

5. GAUSSIAN MIXTURE MODEL:

   1. k-Means Clustering
   2. Gaussian Mixture Model
   3. Bayesian Information Criterion: Balance the goodness of fit of a model with the complexity of the model, penalizing models that are more complex