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: Wednesday 16:00
Google meet class: https://meet.jit.si/Machinelearning-MA
Live tutorial session for online students: Wednesday 16:00
Link on google meet upon request.
Assessment methods
Learning Outcome
At the end of the module you will be able to:
- Critically evaluate the efficacy of advanced data preparation methods and contrast main supervised and unsupervised learning algorithms.
- Design and implement various machine learning algorithms in a range of real-world applications and critically evaluate the outcome of learning on a given problem
- Design and create predictive models of different nature (parametric and non-parametric).
- Articulate decisions, recommendations and other relevant information in a clear, concise presentation tailored to a wide range of audiences within both academic and real-world settings.
For more detail, please see the attached MSG: MSG_2021-22_ML.pdf
Here is the class outline:
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Week description
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svm decision tree
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play
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Simple risk assessment
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Week description
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guideline for your first workflow
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Predictive modeling
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a tree
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spotify prediction
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tennis csv
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Play tennis
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50 euros award
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new tree
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Last exercise
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datasets
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practice
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Build tree
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next class
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reality
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homework
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products with clasification tree
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exercise
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extra zika
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e-Commerce comments analysis
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weather play football
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Beer Wine
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Animals without diet
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Practical knowledge related to machine learning
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Week description
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History and Evolution
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Reality vs. fiction
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Reality vs. fiction
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Auto evaluation 01
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Types of AI
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Types of AI
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Auto evaluation 02
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How does AI work?
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Auto evaluation 03
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How does AI work? classification
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Auto evaluation 04
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Machine Learning, Deep Learning and AI
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Machine Learning, Deep Learning and AI
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Auto evaluation 05
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The importance of data
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The importance of data
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Auto evaluation 06
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Examples
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Auto evaluation 07
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Conclusions
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Use case
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Videogame
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Week description
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R script
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Introduction
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What is Machine Learning?
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Auto evaluation 10
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What is it for?
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What is it for?
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Auto evaluation 11
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What is it for?
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Auto evaluation 12
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What is it for?
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Auto evaluation 13
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Main algorithms
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Logistic regression
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Auto evaluation 14
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K-NN (k-Nearest Neighbors)
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Decision tree
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Auto evaluation 15
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Random Forest
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Naive Bayes
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Auto evaluation 16
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K-Means
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PCA (principal component analysis)
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Auto evaluation 17
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Examples
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Examples
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Deep Learning
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What is it for?
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Auto evaluation 18
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Neural networks
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Auto evaluation 19
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Examples
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Auto evaluation 19b
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Conclusions
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Use case
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Week description
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Introduction
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Required profiles
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Auto evaluation 20
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Required profiles
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Auto evaluation 21
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Required profiles
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The role of the leader
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Auto evaluation 22
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Organization
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Auto evaluation 23
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Organization
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Auto evaluation 24
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Organizational culture
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Auto evaluation 25
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Characteristics of AI projects
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Characteristics of AI projects
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Auto evaluation 26
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Characteristics of AI projects
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Auto evaluation 27
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Methodologies for managing AI projects
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Auto evaluation 28
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Conclusions
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Use case
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Week description
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Introduction
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Human Resources
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Auto evaluation 30
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People analytics
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Auto evaluation 31
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Marketing
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Auto evaluation 32
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Marketing
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Auto evaluation 33
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Legal department
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Auto evaluation 34
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Legal department
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Auto evaluation 35
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Logistics
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Auto evaluation 36
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Logistics
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Operations
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Auto evaluation 37
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Customer Support
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Auto evaluation 38
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Customer Support
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Auto evaluation 39
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Use case
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Week description
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Introduction
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Industry
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Auto evaluation 40
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Farming
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Auto evaluation 41
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Farming
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Auto evaluation 42
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Tourism and restoration
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Auto evaluation 43
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Tourism and restoration
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Auto evaluation 44
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Professional services
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Auto evaluation 45
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Basic notions and tools for creating a chatbot
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Basic notions and tools for creating a chatbot
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Auto evaluation 46
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Use case
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Week description
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Auto evaluation 50
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Week description
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Auto evaluation 60
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Week description
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Auto evaluation 70
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Week description
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Auto evaluation 80
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Week description
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Auto evaluation 90
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Week description
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datathon
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currency identification
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