# Machine Learning Roadmap

This is just a list of topics I want to consider in write about. To some extend, this is the list of topics I want to study in more depth, writing is just an excuse to learn. I will update this list accordingly.

- Support Vector Machines.
- K-means.
- K-neighbors.
- Gradient descent, and other methods of optimization (Adam).
- Recurrent Neural Networks.
- Convolutional Neural Networks.
- Training techniques (Dropout, Normalization)
- Tracking algorithms.
- Detection of Anomalies.
- Reinforcement Learning:
- Q-Learning and Deep Q-Learning.
- Genetic algorithms.
- Genetic programming.
- Simulated annealing.
- Boltzmann machines.
- Historical recount with key papers.
- Gerry Tesauro (1992, 1995)
- Common Examples:
- Tic-Tac-Toe with value function as the probability of winning.