## Stochastic
Optimisation

**Lecturers: **

Ayalvadi Ganesh (a.ganesh@bristol.ac.uk) and Vladislav Tadic (v.b.tadic@bristol.ac.uk)

**Office hours **(second half of unit): Thursday 11am-1pm, Fry Bldg, Room
1.40.

**Texts** (second half of unit):** **

Lecture notes will be provided. In addition, the following texts are
recommended:

·
S. Bubeck and N. Cesa-Bianchi, *Regret
Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems*,
Foundations and Trends in Machine Learning, 2012.

·
T. Lattimore and C. Szepesvari, *Bandit
Algorithms*, Cambridge University Press.

** **

**Homework policy: **Homework is an important part of learning the
material on this course and you are strongly encouraged to attempt all the
homework problems. You may discuss the problems, but you should write out the
solutions on your own.

You will have two items of assessed coursework, which will each count for
10% of the final mark.

**Lecture videos**

Lectures will be in-person. In addition, you can find recorded video
lectures here
covering the second half of the unit. These may not map 1-1 onto the in-person
lectures.

**Lecture notes **

Introduction

The
UCB algorithm

Thompson sampling

**Homework problems**

Problem Sheet 1 Due
Fri, 17 Nov Solutions

Problem Sheet 2 Due
Wed, 29 Nov Solutions

Problem Sheet 3 Due
Mon, 11 Dec Solutions