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Artificial Intelligence: Predictive Modelling in Business (20-21)
Class
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Eugenio Clavijo
Short description: The course introduces the study of Artificial Intelligence (AI) for students in all course streams. It is designed to stand alone as an introduction to AI, but also to provide a background for more advanced study.
Level 6 (Year 4)
Credits: 10 UK credits
Module leader: Eugenio Clavijo
Office hour: 10 am Monday
Google meet class: http://meet.google.com/qqi-gksg-nqz
Assessment methods
- A1.Final Project (100% Coursework)
Please finde the marking grid in the attached file: Marking_Grids_A1_2020.pdf
Learning Outcome
At the end of the module you will be able to:
LO1. Discuss main supervised and unsupervised learning algorithms. (Assessment 1)
LO2. Review further artificial intelligence learning algorithms (Assessment 1)
LO3. Build predictive models of different nature (parametric and non-parametric). (Assessment 1)
For more detail, please see the attached MSG:
Here is the class outline:
Week 1Introduction to Artificial Intelligence and Machine Learning. 3 sections
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Week 2 - Artificial intelligence: types of algorithms21 sections
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Week 3 - Evaluation strategies for machine learning models3 sections
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Week 4 - Linear models5 sections
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Week 5 - Regularization4 sections
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Week 6 - k-Nearest Neighbours4 sections
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Week 7 - Random Forest5 sections
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Week 8 - Support Vector Machines3 sections
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Week 9 - K-Means algorithm6 sections
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Week 10 - Non-negative matrix factorization2 sections
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Week 12 - A* search strategy3 sections
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Week 13-14 - Project submission and Recapitulation, Remarks, Doubts3 sections
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