Module Details |
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
Title | Stochastic Theory and Methods in Data Science | ||
Code | MATH368 | ||
Coordinator |
Dr A Alpers Mathematical Sciences Andreas.Alpers@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2022-23 | Level 6 FHEQ | Second Semester | 15 |
Aims |
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1. To develop a understanding of the foundations of stochastics normally including processes and theory. 2. To develop an understanding of the properties of simulation methods and their applications to statistical concepts. 3. To develop skills in using computer simulations such as Monte-Carlo methods 4. To gain an understanding of the learning theory and methods and of their use in the context of machine learning and statistical physics. 5. To obtain an understanding of particle filters and stochastic optimisation. |
Learning Outcomes |
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(LO1) Develop understanding of the use of probability theory. |
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(LO2) Understand stochastic models and the use statistical data. |
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(LO3) Demonstrate numerical skills for the understanding of stochastic processes. |
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(LO4) Understand the main machine learning techniques. |
Syllabus |
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1 REVIEW OF ESSENTIAL BACKGROUND FROM PROBABILITY/STATISTICS 2 SIMULATION: THEORY AND PRACTICE 3 MARKOV CHAIN METHODS 4 LEARNING THEORY AND METHODS 5. OPTIONAL TOPICS |
Recommended Texts |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
MATH263 Statistical Theory and Methods I; MATH264 STATISTICAL THEORY AND METHODS II; MATH362 APPLIED PROBABILITY; MATH162 INTRODUCTION TO STATISTICS |
Co-requisite modules: |
Modules for which this module is a pre-requisite: |
Programme(s) (including Year of Study) to which this module is available on a required basis: |
Programme(s) (including Year of Study) to which this module is available on an optional basis: |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Final written exam | 120 | 70 | ||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Group project 1 Based on the first part of the module (stochastic theory). | 0 | 15 | ||||
Group project 2 Based on the second part of the module (data science). | 0 | 15 |