## Induction week classes

Date | Subject | Taught by | Materials |
---|---|---|---|

25/09/17 2 - 4pm |
Intro to neuroanatomy and sensory pathways. | Kirsty McNaught Jesse Geerts |
Neuroanatomy slides
Sensory pathway slides |

26/09/17 2 - 3.30pm |
Intro to vector spaces (prerequisites for kernels course) | Michael Arbel Wenkai Xu |
Kernels background notes |

27/09/17 11am - 12.30pm |
Basics of linear algebra | Jorge Menendez Canelas | Linear algebra notes |

27/09/17 2 - 4pm |
Using source control and intro to git | Kirsty McNaught Steve Lenzi Charly Rousseau |
Version control slides |

28/09/17 10.30am - 12.30pm |
Intro to applied Bayesian methods (prerequisites for machine learning course) | Lea Duncker | Stats background notes |

28/09/17 2 - 4 pm |
Basic cell biology and experimental methods | Matt Phillips Steve Lenzi |
Experimental methods |

29/09/17 3 - 4.30pm |
Practical PhD skills and tips, Q&A with students | Everyone | PhD skills notes |

## Additional resources

### Student notes

Below are some notes made by previous students. They may contain errors; you are **strongly** encouraged to report back to us and contribute changes. There is significant overlap in some areas; pick the perspective you prefer.

#### Theoretical Neuroscience

Topic | Link | Comments |
---|---|---|

Full TN Notes | Jorge's TN notes | A somewhat comprehensive set of notes covering the main parts of the course. Should be particularly useful for mean-field theory of networks. Also includes a list of useful papers to read. |

Differential correlations | Jorge's correlations notes | Some handwritten notes on Peter Latham's lecture on differential correlations (Moreno-Bote et al, 2014) |

Learning rules | Learning rules (Kirsty) | Mainly plasticity/hebbian rules, summarised from Dayan&Abbott. |

Biophysics notes | Biophysics (Kirsty) | Covers most of Peter Latham's biophysics, at a higher level than Jorge's more comprehensive notes |

Information theory for TN | Information Theory (TN) (Kirsty) | High level definitions and relationships between Information Theory terms |

#### Machine Learning (including kernels)

Topic | Link | Comments |
---|---|---|

Full ML Notes | Jorge's ML notes | A pretty comprehensive set of notes covering almost everything in the course. There are likely many mistakes. Please find them and tell me. If you would like access to the latex code to update it yourself feel free to ask and I'll give you the Overleaf link. |

Graphical models | Graphical models (Kirsty) | Examples of simple graphical models, understanding independence structures, etc. |

Kernels cribsheet | Kernels notes (Kirsty) | Some useful kernels definitions |

Kernels handwritten notes | Full notes (messy), Comparing Prob Distributions (clean), Convex Optimization & SVMs (clean) | Jorge's handwritten kernels notes (there may be mistakes) |

### Latex template

It is highly recommended that you use latex for your assignment in this course and others. You may find it helpful to start with this sample assignment template, which demonstrates some figures, maths and other useful things.