Why?

Machine learning has escaped from the cage of perception. A growing number of state-of-the-art systems from field robotics, acrobatic autonomous helicopters, to the leading computer Go player and walking robots rely upon learning techniques to make decisions. This change represents a truly fundamental departure from traditional classification and regression methods as such learning systems must cope with a)

their own effects on the world, b) sequential decision making and long control horizons, and c) the

exploration and exploitation trade-off. In the last 5 years, techniques and understanding of these have developed dramatically. One key to the advance of learning methods has been a tight integration with

optimization techniques, and as such our case studies will focus on this.

What? (Things we will cover)