Undergraduate Teaching 2019-20

Engineering Tripos Part IB, 2P8: Information Engineering, 2018-19

Engineering Tripos Part IB, 2P8: Information Engineering, 2018-19

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Lecturers

Professor R Cipolla, Dr R Turner, Dr G Vinnicombe

Timing and Structure

Easter Term: Weeks 1-4 - 14 lectures + 2 examples classes, 4 lectures/week

Aims

The aims of the course are to:

  • Provide a unified view of information engineering showing how signal processing, computer vision, machine learning and control relate to one another.
  • Use example applications drawn from autonomous driving to provide concrete examples of important concepts and subareas of information engineering including computer vision, machine learning and reinforcement learning
  • Introduce computer vision including algorithms for 3D reconstruction, registration and object recognition
  • Introduce basic concepts in inference, learning and optimisation including maximum-likelihood estimation, Bayes’ rule and gradient descent.
  • Introduce basic algorithms for planning and the general area of sequential decision making / reinforcement learning

Objectives

As specific objectives, by the end of the course students should be able to:

  • Provide example applications of machine perception, machine learning, and autonomous decision making systems.
  • Understand the mathematical basis for perspective projection and feature detection; neural networks and parameter estimation; basic planning and reinforcement learning.
  • Implement methods to solve simple computer vision and machine learning problems including object detection and segmentation and sequential decision making.

Content

A: Introduction to Autonomous Driving (1L) (Guest lecturer from industry - J. Hawkes)

  • The anatomy of a self-driving car with description of autonomous driving hardware (the car, sensors, interfaces and actuators) 
  • Motivate the need for machine perception (computer vision), learning and decision making systems
  • Important sub-problems in the data processing pipeline: object detection, localisation and mapping, prediction, planning and action
  • Interaction with an example of a self-driving car in Cambridge

B: Machine Perception: Introduction to Computer Vision (6L) (R. Cipolla)

  • An introduction to computer vision: reconstruction, registration and recognition
  • Perspective projection
  • Convolution with gaussians and derivatives of gaussians to provide bandpass filters.
  • Edge detection using directional filters.
  • Scale-space and image pyramids for feature detection
  • The SIFT feature descriptor for matching image features.
  • Demonstration of state-of-the-art object detection, semantic segmentation and localisation systems
  • Examples paper and class

C: Machine Learning: Introduction to Deep Learning (5L) (R. Turner)

  • Training a simple classifier: logistic regression and gradient descent
  • Neural networks: Multi-layer perceptrons and back propagation
  • Neural networks: convolutional neural networks
  • Anatomy and training of a convolutional neural network in Tensorflow or PyTorch - network architecture, loss function, weight initialization, batch size, learning rate, epochs.
  • Examples paper and class 

D: Autonomous Decision Making: Introduction to Planning and Reinforcement Learning (4L) (G. Vinnicombe and A. Kendall)

  • Introduction to planning: Shortest path problems, value functions and dynamic programming
  • Introduction to reinforcement learning: Q-learning, actor-critic methods, approximations using neural networks
  • Application of these methods to a self-driving car
  • Demonstration of perception and planning algorithms running on a self-driving car
  • Identification of open problems
  • Examples paper

Booklists

Please see the Booklist for Part IB Courses for references for this module.

Examination Guidelines

Please refer to Form & conduct of the examinations.

Last modified: 10/05/2019 15:22