**Collection Number**C19011

**Collection Date**-

**Collection Type**Course

## PSI 2018/2019 - Machine Learning - Lecture 15

Generative modelling: explicit and approximate likelihood models; implicit likelihood models

## PSI 2018/2019 - Machine Learning - Lecture 14

Generative modelling: explicit and tractable likelihood models

## PSI 2018/2019 - Machine Learning - Lecture 13

Quantum state reconstruction using restricted Boltzmann machines

## PSI 2018/2019 - Machine Learning - Lecture 12

Restricted Boltzmann machines: training to minimize the Kullback-Liebler divergence

## PSI 2018/2019 - Machine Learning - Lecture 11

Generative modelling: Hopfield networks, Boltzmann machines and restricted Boltzmann machines (RBMs)

## PSI 2018/2019 - Machine Learning - Lecture 10

Reinforcement learning: Q-learning, Bellman equations

## PSI 2018/2019 - Machine Learning - Lecture 9

Reinforcement learning: Markov decision processes, policy gradient methods

## PSI 2018/2019 - Machine Learning - Lecture 8

Dimensional reduction using t-distributed stochastic neighbour embedding (t-SNE); Kullback-Liebler (KL) divergence; maximum likelihood estimation

## PSI 2018/2019 - Machine Learning - Lecture 7

Introduction to unsupervised learning; dimensional reduction using principal component analysis

## PSI 2018/2019 - Machine Learning - Lecture 6

Convolutional neural networks: local receptive fields, shared weights and biases, pooling