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Machine Learning (CSU44061/CS7CS4/CS4404)

Students who complete this module should be able to (1) Decide when machine learning is an appropriate method to solve a problem (2) Understand how machine learning works. (3) Apply machine-learning frameworks to solve real-world problems, and adjust and extend existing algorithms when necessary. The module content includes

  1. Machine Learning Basics (Application Areas, Challenges, Alternatives to Machine Learning)
  2. Machine Learning in Action (Datasets, Frameworks, Evaluation)
  3. Cross-validation and confidence intervals
  4. Overfitting/underfitting (bias-variance trade-off)
  5. Machine Learning Algorithms
    1. Linear Regression
    2. Logistic Regression
    3. Support Vector Machines
    4. Kernel Methods
    5. k-Means Clustering and Mixture Models for Unsupervised Learning
    6. Neural Networks
    7. Deep Learning Algorithms
  6. Use of gradient descent, and extensions for improved scalability (stochastic gradient descent etc)
  7. Probabilistic interpretations of ML algorithms.
  8. Maximum Likelihood and MAP estimators.
  9. Recommender systems

e-Business II (CS3BC2)

Students who complete this module will be able to analyse technical choices related to the design and platform selection for e-business application in relation to the business context and requirements. In addition, they will be able to design and program elements of web and mobile-based applications and web services to address an e-business problem. Module Content

  1. Evolution of the WWW as a business platform
  2. Web Application Servers and 3-tier enterprise architectures
  3. Web usage analytics and handling of personal data
  4. Web Services for e-business interoperability
  5. Workflow and web service composition for business-to-business applications
  6. Online social networks as business-to-consumer application platforms
  7. Enterprise Linked Data and the Semantic Web

Previous Lectures

Mobile Technologies

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Data Handling

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Programming & Modelling

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IT Project Management

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