Advanced Topics in Machine Learning
Andreas Krause and Daniel Golovin course at CalTech. Lecture notes, readings, this will keep you entertained for some time.
Overview:
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?
Two reasons:
1) Machine learning techniques are incredibly useful in appropriate cases.
2) You have to understand machine learning to pick out the appropriate cases.