Artificial Intelligence and Predictive Modelling
Class
![](/files/95711/resize-182(3)(2).jpg?lmsauth=89eb008c5cd5ede0e25210f724623c1776cbd09c)
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
Module leader: Eugenio Clavijo
Office hour: 16 on Wednesday
Google meet class: https://meet.jit.si/ArtificialIntelligenceandPredictiveModelling
Live tutorial session for online students: Wednesday 16:00
Link on google meet upon request.
Assessment methods
- A1.Final Project (100% Coursework)
Please finde the marking grid in the attached file: Marking_Grids_A1_2021.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:
Join the class online1 section
|
|
|
Week 1 Introduction4 sections
|
||||
|
Week 2 - Artificial intelligence: types of algorithms22 sections
|
||||||||||||||||||||||
|
Week 3 - Evaluation strategies for machine learning models3 sections
|
|||
|
Week 4 - Linear models5 sections
|
|||||
|
Week 5 - Regularization5 sections
|
|||||
|
Week 6 - k-Nearest Neighbours4 sections
|
||||
|
Week 7 - Random Forest6 sections
|
||||||
|
Week 8 - Support Vector Machines3 sections
|
|||
|
Week 9 - K-Means algorithm6 sections
|
||||||
|
Week 10 - Non-negative matrix factorization2 sections
|
||
|
Week 11 - Evolutionary Algorithms: representation and heuristic optimization3 sections
|
|||
|
Week 12 - A* search strategy4 sections
|
||||
|
Week 13-14 - Project submission and Recapitulation, Remarks, Doubts2 sections
|
||
|