There are multiple ways to approach solving game playing problems. Some games can be solved by search algorithms for example. This works well for card and board games up to some level of complexity. For instance, IBM's Deep Blue was essentially a fast heuristic-driven search for optimal moves.
However, probably the most generic machine learning algorithm for training an agent to perform a task optimally is reinforcement learning. Technically it is not one algorithm, but an extended family of related algorithms that all solve a specific formalisation of the learning problem.
Informally, Reinforcement Learning (RL) is about finding optimal solutions to problems defined in terms of an agent that can observe the state of an environment, take actions in that environment and experience rewards which are somehow related to the state and action. RL solvers need to be designed to cope with situations where rewards are received later than when important actions were taken, and this is usually achieved by the algorithm learning an internal expectation of later rewards associated with state and/or state-action pairs.
Here are some resources for studying Reinforcement Learning:
You will find the subject itself is quite large as more and more sophisticated variations of the algorithms are necessary as the problem to solve becomes harder.
Starting games for studying reinforcement learning might include:
Tik-tac-toe (aka Noughts and crosses) - this can be solved easily using search, but it makes for a simple toy problem to solve using basic RL techniques.
Mazes - in the reinforcement learning literature, there are many examples of "grid world" games where an agent moves in single N,E,S,W steps on a small board that can be populated with hazards and goals.
Blackjack (aka 21)
If you want to work with agents for playing video games, you will also want to learn about neural networks and probably in some detail - you will need deep, convolutional neural networks to process screen graphics.
A relatively new resource for RL is OpenAI Universe. They have done a lot of work to package up environments ready to train agents against, meaning you can concentrate on studying the learning algorithms, as opposed to the effort of setting up the environment.
Regarding your list of current skills: None of them are directly relevant to reinforcement learning. However:
If you can understand the maths and theory from your previous course, then you should also be able to understand reinforcement learning theory.
If you have studied any online or batch supervised learning techniques, then these can be used as components inside a RL framework. Typically they can be used to approximate a value function of the game state, based on feedback from successes and failures so far.