Introduction to inference

Introduction to Linear Regression

Introduction to Bayesian Linear Regression

Introduction to Classification

Examples questions and solutions

Sequence modelling

Clustering and the EM algorithm

Convolutional neural networks

Sequential Monte Carlo

Deep Learning and Probabilistic Modelling

Database slides

Database handout

Motivational example

example SQL database

Student Teacher PDF

SQL example text file of demos

Concurrency control handout

Distributed systems case studies

Database examples sheet

Database examples sheet crib

Lab handout

Lecture 2: Neural networks for computer vision

Lecture 3: Convolutional neural networks for computer vision

Machine learning for computer vision examples sheet

Handout 2: Feature Extraction

Handout 3: Feature Descriptors

Handout 4: Search

Handout 5: Visual words Slides lecture 1: edge detection

Slides lecture 2: 2D edge detection

Slides lecture 3: corner detection

Slides lecture 4: blobs and feature descriptors

Slides lecture 5: search

Slides lecture 6: visual words examples sheet

Matlab Demos

Lecture 2 slides: Gaussian quiz and primary visual cortex

Lecture 3 slides: 2D edge detection and moving beyond edges

Lecture 4 slides: corner detection

Lecture 5 slides: blobs and feature descriptors

Matlab Demos

Examples Sheet 1

Examples Sheet 1 solutions

Lecture 6 slides: Perspective Projection and the Pin Hole Camera

Lecture 7 slides: Homogeneous coordinates

Lecture 8 slides: The projective camera and planar projection

Lecture 9 slides: Inverting the imaging process

Lecture 10 slides: the affine camera and invariants

Perspective projection of a circle on the ground plane

RANSAC Pseudocode

Auto-stitch: SIFT, homography and RANSAC

Examples Sheet 2

Examples Sheet 2 solutions

Lecture 12 slides: Stereo vision and epipolar geometry

Lecture 14 slides: stereo vision with uncalibrated cameras

Examples Sheet 3

Examples Sheet 3 solutions

Lecture 15 slides: Random forests

Handout: Random forests

Handout: weighing problem

Handout: decision boundaries

Lecture 16 slides: Neural networks

Handout: Neural networks

Lecture 1 handout: Component reliability

Lecture 2 slides: System Reliability

Lecture 2 handout: System Reliability

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