Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23

Dimensionality reduction Wherein I teach myself, amongst other things, feature selection, how a sparse PCA works, and decide where to file multidimensional scaling 2015-03-22 – 2020-06-23

Deep fakery 2020-06-15

Model fairness 2018-11-29 – 2020-06-14

Probabilistic neural nets Machinelearnese for “Bayesian” 2017-01-11 – 2020-06-14

Model interpretation 2016-09-01 – 2020-06-14

Natural language processing Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move. 2018-01-11 – 2020-06-07

Voice fakes 2018-09-06 – 2020-05-27

Voice transcriptions 2019-01-08 – 2020-05-16

Directed graphical models 2017-09-20 – 2020-05-13

AV controller interfaces 2019-12-10 – 2020-05-11

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2020-05-01

Statistics and machine learning 2011-04-15 – 2020-04-26

Convolutional neural networks 2017-11-10 – 2020-04-24

Likelihood free inference 2020-04-22

Machine learning for physical sciences Turbulent mixing at the boundary between two disciplines with differing inertia and viscosity 2017-05-15 – 2020-04-22

Learning summary statistics 2020-04-22

Learning graphical models from data What is independent of what? 2017-09-20 – 2020-04-11

Analysis/resynthesis of audio 2016-01-15 – 2020-04-09

Neural nets designing the fanciest usable differentiable loss surface 2016-10-14 – 2020-04-05

Deep learning as a dynamical system 2018-08-13 – 2020-04-02

Teaching computers to write music 2016-06-06 – 2020-03-25

Here’s how I would do art with machine learning if I had to 2016-06-06 – 2020-03-24

Diagramming and visualising graphical models my need for this is conditionally dependent upon my deadline, given the subject matter 2018-03-29 – 2020-02-26

Learnable indexes and hashes 2018-01-12 – 2020-02-18

pytorch #torched 2018-05-04 – 2020-02-06

Recurrent neural networks 2016-06-16 – 2020-01-23

Learning in adaptive systems On staring into scopophilic abysses 2019-10-19 – 2020-01-22

Factor graphs 2019-12-16

Controllerism Making thing happen by waving your arms about on stage 2014-11-17 – 2019-12-10

Audio source separation 2019-11-04 – 2019-11-26

Tunings 2015-10-29 – 2019-11-18

Auditory features descriptors, maps, representations for audio 2019-11-13

Machine listening Statistical models for audio 2014-10-10 – 2019-11-12

Javascript machine learning 2017-01-13 – 2019-11-09

ISMIR 2019 Music Nerds in Delft 2019-11-04 – 2019-11-09

Inference on graphical models Given what I know about what I know, what do I know? 2017-09-20 – 2019-10-28

Undirected graphical models 2017-09-20 – 2019-10-28

Bio computing 2016-05-29 – 2019-10-14

Why does deep learning work? despite the fact we are totally just making this shit up 2017-05-30 – 2019-10-09

Probabilistic graphical models over continuous index sets 2014-08-05 – 2019-09-25

Differentiable learning of automata 2016-10-14 – 2019-09-11

Hierarchical models DAGs, multilevel models, random coefficient models, mixed effect models… 2015-06-07 – 2019-08-19

Causal inference Confounding! This scientist performed miracle graph surgery during an intervention and you won’t believe what happened next 2016-10-26 – 2019-08-07

Compressing neural nets 2016-10-14 – 2019-05-27

Neural nets designing the fanciest usable differentiable loss surface 2016-10-14 – 2019-05-27

Mind reading by computer The ultimate inverse problem 2017-07-01 – 2019-01-21

Overparameterization a.k.a. improper learning 2018-04-04 – 2018-12-11

Machine vision 2015-01-03 – 2018-11-14

Gesture recognition 2014-10-17 – 2018-11-12

Compressing artificial neural networks 2016-10-12 – 2018-10-23

Moral calculus 2014-08-04 – 2017-11-21

Distributed sensing and swarm sensing 2014-10-13 – 2017-10-12

Probabilistic graphical models 2014-08-05 – 2017-09-11

Brains Neural networks made of real neurons, in functioning brains 2014-11-03 – 2017-08-15

Quantum-probabilistic graphical models 2017-08-07

Generalisation in neural networks Generalisation for street fighters 2017-02-12 – 2017-06-20

Marketing psychology 2017-04-27 – 2017-05-29

Entity embeddings 2017-04-01

Musical metrics and manifolds 2014-09-26 – 2017-03-27

Random neural networks 2017-02-17 – 2017-02-19

Neural network activation functions 2017-01-12

Genetic algorithms 2011-04-06 – 2016-12-28

Greatest hits 2016-11-19 – 2016-12-12

UNSW Stats reading group 2016 - Causal DAGs An introduction to conditional independence DAGs and their use for causal data. 2016-10-17 – 2016-10-21

Inference from disorder 2016-10-19

Generalised linear models 2016-03-24 – 2016-08-31

Probably Approximately Correct 2014-11-24 – 2016-05-29

Indirect inference 2014-12-23 – 2015-12-15

Pattern machine 2011-06-27 – 2015-11-24

Grammatical inference 2011-05-30 – 2015-02-19