Project Suggestions
Deep learning project. Be creative in finding a new learning challenge. Some useful pointers: interesting start here with a related post here, and for quite a bit more detail see here.
Deep learning for the game of Hex (here and here, with code here (open source)). Hex is a rather interesting board game with some of the flavor of Go but easier to study and model. Also, for smaller size boards there are known optimal moves.
Can a deep learning approach get close to optimal? What about game tree Monte Carlo search (UCT)? Insights into these questions should provide insights into AlphaGo.
3-D-Tic-Tac-Toe. Evaluate multiple forms of play. Deep-learn features?
Learning to Play Checkers
Implementing Virtual Agents in a Virtual Environment
Learning to Play Checkers
Build a system that plays your favorite game in which each player uses a different strategy. Compare strategies. E.g., one player uses a random strategy, one uses a set of hand-coded rules, and the others use heuristic methods with different static evaluation functions.
Apply a genetic algorithm to the problem of automatic generation of computer programs.
Apply a genetic algorithm to the problem of learning natural language grammars.
Build a system that uses heuristic search (with minimax and alpha-beta pruning) to play Connect-4. Evaluate it against itself with different levels of search.
Build (and train) a system that plays Connect-4 using a neural network. Can you deep-learn useful features?
A theorem-proving system for some (small) subset of mathematics.
A computer player for bridge or poker. The chance component adds an interesting search dimension. Check first what is out there!
A program that generates automatic crossword puzzles, starting from a dictionary and an empty board.
Recreate from its specifications the reinforcement learning (neural net) system (Tesauro, 1992) that learns to play backgammon by playing games against itself.