Machine Learning -
Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.
Supervised Learning
- Logistic regression
- Perceptron
- Exponential family
- Generative learning algorithms
- Gaussian discriminant analysis, Naive Bayes
- Support vector machines
- Model selection and feature selection
- Ensemble methods: Bagging, boosting
- Evaluating and debugging learning algorithms
Unsupervised learning
- Clustering
- K-means
- EM. Mixture of Gaussians
- Factor analysis
- PCA (Principal components analysis)
- ICA (Independent components analysis)
Reinforcement learning and control
- MDPs. Bellman equationss
- Value iteration and policy iteration
- Linear quadratic regulation (LQR). LQG
- Q-learning. Value function approximation
- Policy search. Reinforce. POMDPs.