This is the homeworks from this Machine learning course. Tasks are implementing different machine learning algorithms including supervised, unsupervised, random optimization, reinforcement learning.
- Using R to perform various supervised learning algorithms:
- Decision Tree with Pruning
- Neural Network
- Boosting
- SVM (support vector machine)
- kNN (k-nearest neighbor)
- Random Hill Climbing
- Simulated Annealling
- Genetic Algorithm
- MIMIC
- K-means
- Expectation Maximization
- Dimensionality reduction using Principal Component Analysis, Independent Component Analysis, Random Projection, Linear Discriminant Analysis.
This section assignment is to design and solve two MDP (Markov Decision Process) problem (Grid World) using
- value iteration
- policy iteration
- Q learning.