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Spring semester - MSc - IB Calendar view

Machine learning - MA


Class
Eduardo Rivera
Enrolment for this class is currently closed.

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.

Artificial Intelligence Description

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:

  1. Critically evaluate the efficacy of advanced data preparation methods and contrast main supervised and unsupervised learning algorithms.
  1. 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
  2. Design and create predictive models of different nature (parametric and non-parametric).
  3. 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:

    Join the class online

    1 section
    Join the class online

    Week 1 - Introduction to Artificial Intelligence and Machine Learning.

    4 sections
    Week description
    svm decision tree
    play
    Simple risk assessment

    Week 2 - Artificial intelligence: types of algorithms

    24 sections
    Week description
    guideline for your first workflow
    Predictive modeling
    a tree
    spotify prediction
    tennis csv
    Play tennis
    50 euros award
    new tree
    Last exercise
    datasets
    practice
    Build tree
    next class
    reality
    homework
    products with clasification tree
    exercise
    extra zika
    e-Commerce comments analysis
    weather play football
    Beer Wine
    Animals without diet
    Practical knowledge related to machine learning

    Week 3 - Use of Artificial Intelligence

    23 sections
    Week description
    History and Evolution
    Reality vs. fiction
    Reality vs. fiction
    Auto evaluation 01
    Types of AI
    Types of AI
    Auto evaluation 02
    How does AI work?
    Auto evaluation 03
    How does AI work? classification
    Auto evaluation 04
    Machine Learning, Deep Learning and AI
    Machine Learning, Deep Learning and AI
    Auto evaluation 05
    The importance of data
    The importance of data
    Auto evaluation 06
    Examples
    Auto evaluation 07
    Conclusions
    Use case
    Videogame

    Week 4 - Linear models

    35 sections
    Week description
    R script
    Introduction
    What is Machine Learning?
    Auto evaluation 10
    What is it for?
    What is it for?
    Auto evaluation 11
    What is it for?
    Auto evaluation 12
    What is it for?
    Auto evaluation 13
    Main algorithms
    Logistic regression
    Auto evaluation 14
    K-NN (k-Nearest Neighbors)
    Decision tree
    Auto evaluation 15
    Random Forest
    Naive Bayes
    Auto evaluation 16
    K-Means
    PCA (principal component analysis)
    Auto evaluation 17
    Examples
    Examples
    Deep Learning
    What is it for?
    Auto evaluation 18
    Neural networks
    Auto evaluation 19
    Examples
    Auto evaluation 19b
    Conclusions
    Use case

    Week 5 - Implementation

    24 sections
    Week description
    Introduction
    Required profiles
    Auto evaluation 20
    Required profiles
    Auto evaluation 21
    Required profiles
    The role of the leader
    Auto evaluation 22
    Organization
    Auto evaluation 23
    Organization
    Auto evaluation 24
    Organizational culture
    Auto evaluation 25
    Characteristics of AI projects
    Characteristics of AI projects
    Auto evaluation 26
    Characteristics of AI projects
    Auto evaluation 27
    Methodologies for managing AI projects
    Auto evaluation 28
    Conclusions
    Use case

    Week 6 - Application of AI to the company

    24 sections
    Week description
    Introduction
    Human Resources
    Auto evaluation 30
    People analytics
    Auto evaluation 31
    Marketing
    Auto evaluation 32
    Marketing
    Auto evaluation 33
    Legal department
    Auto evaluation 34
    Legal department
    Auto evaluation 35
    Logistics
    Auto evaluation 36
    Logistics
    Operations
    Auto evaluation 37
    Customer Support
    Auto evaluation 38
    Customer Support
    Auto evaluation 39
    Use case

    Week 7 - Examples of AI in the main sectors

    18 sections
    Week description
    Introduction
    Industry
    Auto evaluation 40
    Farming
    Auto evaluation 41
    Farming
    Auto evaluation 42
    Tourism and restoration
    Auto evaluation 43
    Tourism and restoration
    Auto evaluation 44
    Professional services
    Auto evaluation 45
    Basic notions and tools for creating a chatbot
    Basic notions and tools for creating a chatbot
    Auto evaluation 46
    Use case

    Week 8 - Test Use of Artificial Intelligence

    2 sections
    Week description
    Auto evaluation 50

    Week 9 - Test Linear models

    2 sections
    Week description
    Auto evaluation 60

    Week 10 - Test Implementation

    2 sections
    Week description
    Auto evaluation 70

    Week 11 - Test Application of AI to the company

    2 sections
    Week description
    Auto evaluation 80

    Week 12 - Test Examples of AI in the main sectors

    2 sections
    Week description
    Auto evaluation 90

    Week 13-14 - Recapitulation, Remarks, Doubts

    3 sections
    Week description
    datathon
    currency identification