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.

Content:

• 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.