Learning Outcomes Week 1

In this lecture, the following concepts were introduced.

• The ability to identify likely applications of artificial intelligence and machine learning in modern computers including:
• Targeted adverts
• Speech recognition
• Ranking of news feed items
• suggestion of likely friends in social networks
• Pose identification in games consoles
• Recommender systems, like Amazon and Netflix
• Face recognition systems such (Picasa, Google, Facebook)
• Modern artificial intelligence is heavily reliant on data.
• Machine learning requires that data is combined with assumptions (or a model) to make a prediction.
• The history of prediction with data goes back as far as Laplace and Gauss (200 years ago).
• Many of the principles of prediction haven't changed much in recent years, but the availability of data and computing power has greatly increased.

Learning Outcomes Week 2

In this lecture, the following concepts were introduced.

• An overview of the idea of classification. Including
  • Understanding a basic classification algorithm like the perceptron algorithm
  • Understanding what a feature matrix is.
  • Understand what the data labels are.
  • The concept of a learning rate
  • The concept of linear separability
• An overview of the idea of regression. Including
  • Basis functions can be used to make a linear regression non-linear.
  • An example of a commonly used basis set (like polynomials or radial basis functions).
  • A commonly used error (or objective) function such as the sum of squared errors.
• The difference between a model and an algorithm
• The concept of generalization
• The idea of a training set
• The use of the error function (also known as an objective function)
• The importance of the mathematical concepts of
  • vectors
  • differentiation
  • minimum
• The idea behind the optimization approach of steepest descent.
• How stochastic gradient descent differs from steepest descent and why this is important.

Learning Outcomes Week 4

In this lecture, the following concepts were introduced.

• The difference between unsupervised learning and supervised learning.
• Examples of unsupervised learning approaches:
  • Clustering: e.g. k-means clustering
  • Dimensionality reduction: e.g. principal component analysis.
• The algorithms for dimensionality reduction and clustering involve optimisation of objective functions.
• The different characteristics of these approaches to dimensionality reduction: in clustering you represent your data as discrete groups, in dimensionality reduction by a reduced number of continuous variables.
• Understand that machine learning has two broad approaches
  • The Optimization Approach to ML
  • The Probabilistic Approach to ML
  And that these approaches are related: often the error function has a probabilistic interpretation through being the negative log likelihood.
• The basic probability rules including:
  • The properties of a probability distribution.
  • The sum rule of probability.
  • The product rule of probability.
  • Bayes' rule
• How these rules are applied in a simple robot navigation example.
• The difference between a machine learning model and a machine learning algorithm.

Learning Outcomes Week 5

This lecture covers the following learning outcomes

• A review of continuous probability densities.
• A review of the Gaussian density.
• The equivalence between least squares and a Gaussian noise approximation.

Learning Outcomes Week 6

This lecture covers the following learning outcomes

• Mapping the basic programming concepts into algorithms for machine learning.
  • Ability to make small modifications to existing code to change an algorithm.
• Be able to relate lines in a programming language to mathematical formulae.
  • Understanding that the mathematical derivations we create can map to implementations in code.
  • Understanding how mathematics is implemented as code, for example data structures like arrays can map to mathematical structures like vectors.
• Understanding the particular needs when interacting with data: an environment that allows the display of the data. (e.g. IPython notebook).
• Reinforcing the previous lectures' learning outcomes.

This document last modified Friday, 24-Jan-2014 08:12:01 UTC.