Computational Perception Group

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AI4ER 0: Introduction to Machine Learning

Introduction to the course
Introduction to inference
Introduction to Linear Regression
Introduction to Bayesian Linear Regression
Introduction to Classification
Examples questions and solutions

3F8: Inference

Introduction to inference
Sequence modelling
Clustering and the EM algorithm

4F12: Inference

Multi-layer perceptrons
Convolutional neural networks

MLMI4: Advanced Machine Learning

Dimensionality Reduction
Sequential Monte Carlo
Deep Learning and Probabilistic Modelling

Old courses

3F6: Software Engineering and Design

Elena's course webpage

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

4F12: Computer vision

Lecture 1: decision trees, information theory and random forests
Lecture 2: Neural networks for computer vision
Lecture 3: Convolutional neural networks for computer vision
Machine learning for computer vision examples sheet

Paper 8: Computer vision

Handout 1: Introduction
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

4F12: Computer Vision and Robotics

Part 1: Feature detection
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

Part 2: Perspective Projection
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

Part 3: Stereo vision
Lecture 12 slides: Stereo vision and epipolar geometry
Lecture 14 slides: stereo vision with uncalibrated cameras
Examples Sheet 3
Examples Sheet 3 solutions

Part 4: Machine vision
Lecture 15 slides: Random forests
Handout: Random forests
Handout: weighing problem
Handout: decision boundaries
Lecture 16 slides: Neural networks
Handout: Neural networks

computer vision research video demonstrations

Nuclear Energy MPhil: Reliability

Lecture 1 slides: Component reliability
Lecture 1 handout: Component reliability
Lecture 2 slides: System Reliability
Lecture 2 handout: System Reliability

3G3: Introduction to neuroscience

Hearing: slides and handout

4G3: Computational neuroscience

Representational learning: slides, handout

4F13: Machine Learning

Course webpage

Paper 8: Machine Learning

Machine learning: Zoubin's slides and the mixture of Gaussians example slides.
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