Advanced Topics in Machine Learning
Andreas Krause and Daniel Golovin course at CalTech. Lecture notes, readings, this will keep you entertained for some time.
How can we gain insights from massive data sets?
Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In particular, in this course we will study:
- Online learning: How can we learn when we cannot fit the training data into memory? We will cover no regret online algorithms; bandit algorithms; sketching and dimension reduction.
- Active learning: How should we choose few expensive labels to best utilize massive unlabeled data? We will cover active learning algorithms, learning theory and label complexity.
- Nonparametric learning on large data: How can we let complexity of classifiers grow in a principled manner with data set size? We will cover large-scale kernel methods; Gaussian process regression, classification, optimization and active set methods.
Why would a non-strong AI person list so much machine learning stuff?
1) Machine learning techniques are incredibly useful in appropriate cases.
2) You have to understand machine learning to pick out the appropriate cases.