ULMS Electronic Module Catalogue |
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 | Machine Learning and Big Data Econometrics | ||
Code | ECON701 | ||
Coordinator |
Dr GD Liu-Evans Economics Gareth.Liu-Evans@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2021-22 | Level 7 FHEQ | Second Semester | 15 |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
ECON814 ECONOMETRIC AND STATISTICAL METHODS |
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: |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
12 |
6 |
12 6 |
36 | |||
Timetable (if known) |
60 mins X 1 totaling 12
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60 mins X 1 totaling 6
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60 mins X 1 totaling 12
60 mins X 1 totaling 6 |
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Private Study | 114 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Examination Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes | 24 hours | 50 | ||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Individual Data Analysis Report Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes | -1000 words | 50 |
Aims |
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The module aims to prepare students for careers where a good understanding of Machine Learning methods and Python programming is necessary or advantageous. Examples include: research careers in applied economics or finance, careers in data science, or careers in data analysis generally. |
Learning Outcomes |
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(LO1) Students will be able to define, explain and motivate a number of Machine Learning methods. |
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(LO2) Students will be able to use libraries in Python for Machine Learning and scientific research. |
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(LO3) Students will be able to produce Jupyter Notebook documents, mixing formatted text in Markdown with Python code. |
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(LO4) Students will gain a good general ability with the Python programming language. |
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(S1) Flexibility and adaptability |
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(S2) Problem solving |
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(S3) Numeracy |
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(S4) Commercial awareness |
Teaching and Learning Strategies |
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Hybrid delivery, with social distancing on campus. 1 hour online asynchronous learning per week x 12 weeks Students will need to spend time studying the machine learning methodology, along with the code examples and online documentation. Significant time will also need to be spent coding in Python, in order to gain confidence with the facilities and in order to complete the coursework assessment. |
Syllabus |
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Main topics: Least Squares review, and Subset Selection methods; Principal Components regression; Lasso and related methods; Regression trees and random forests; General principles for building predictive Machine Learning models; Econometric inference in high dimensional models; Deep Learning – several sessions covering algorithmic details (backpropagation and gradient descent, stochastic gradient descent, regularisation), and the use of Python libraries for deep learning; Applications of Big Data in Economics. Python libraries (indicative examples): numpy, pandas, scikit-learn, pytorch/keras, nltk. Programming environments: Jupyter Notebook, Spyder. Students are encouraged to install the Anaconda distribution of Python 3, which is freely available, as this comes with Jupyter Notebook, Spyder, and many of the required libraries. Learning resources will be available on Canvas, e.g. Jupyter Notebooks containing code from lectures and lab sessions. Participants are encouraged to read the recommended textbooks for further detail about the Machine Learning methodologies and their application in Python, and references will be given in class. There are many good textbook treatments of the topics covered in the module. The websites for the Python packages and libraries generally have introductory examples, tutorials and documentation, and participants on the module are encouraged to work through these. The R language will be introduced and used for one topic, where the package hdm will be used. |
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. |