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CM50265: Machine learning 2

Academic Year: 2018/9
Owning Department/School: Department of Computer Science
Credits: 6      [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CW 100%
Assessment Detail:
  • Written Report + Presentation (CW 40%)
  • Completed Lab Reports (CW 60%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
Description: Aims:
This unit covers the breadth of machine learning topics as well as providing detailed treatment of advanced methods that are representative of the different categories of ML approaches.

Learning Outcomes:
At the end of this unit, students will be able to:
* Demonstrate a systematic knowledge of state-of-the-art ML approaches and an awareness of the latest ongoing research in the field
* Develop and evaluate critically advanced ML models for real-world problems
* Identify and implement appropriate and original algorithms to perform inference
* Make predictions from models and account for uncertainty

Skills:
Intellectual skills:
* Demonstrate an advanced conceptual understanding of ML modelling (T, F, A)
* Critical analysis of advanced models and algorithms (T, F, A)
Practical skills:
* Produce practical implementations of advanced ML algorithms (T, F, A)
* Evaluate and critique algorithms on complex data (T, F, A)
Transferable skills:
* Numerical programming and independent learning (F, A)
* Technical report writing and presentation skills (F, A)

Content:
Topics covered will normally include: Bayesian approaches to ML, graphical models (e.g. Markov random fields), Bayesian non-parametric models (e.g. Gaussian processes), deep learning (e.g. neural networks), time series (e.g. hidden Markov models), sparse models (e.g. compressed sensing), and unsupervised learning (e.g. density estimation).
Programme availability:

CM50265 is Compulsory on the following programmes:

Department of Computer Science

Notes:

  • This unit catalogue is applicable for the 2018/19 academic year only. Students continuing their studies into 2019/20 and beyond should not assume that this unit will be available in future years in the format displayed here for 2018/19.
  • Programmes and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Undergraduates: .
  • Postgraduates: .