IntroML_1_Introduction

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Introduction to
Machine Learning
(5 ECTS)
Giovanni Di Liberto
Asst. Prof. in Intelligent Systems, SCSS
Room G.15, O’Reilly Institute ©Trinity College Dublin

2. Trinity College Dublin, The University of Dublin
Quote Slide Option 2 Lecture outline
• Module outline and schedule
• Module objectives and evaluation criteria
• What is Machine Learning (ML)
• What can we do with ML

3. Trinity College Dublin, The University of Dublin
Quote Slide Option 2 Course outline
• (Introduction to programming – Python)
• Data visualisation
• Supervised learning
• Classification
• Regression and time-series
• Overfitting and regularisation
• Unsupervised learning
• Clustering
• Dimensionality reduction
• Model tuning, evaluation, and Feature selection
• Tutorials + continuous assessment on lab exercises
• Examples on publicly available data
• Discussion about current and future challenges for ML
• E.g., data sharing, anonymisation and privacy, standardisation

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Week LECTURE LAB Additional hour and deadlines
1 Overview Introduction to programming and Python
programming tutorial
2 Descriptive stats vs. ML. Data visual.
Discussion board task explanation
Python programming and basic
visualisation tutorial
3 Supervised Learn. Simple classifiers Data visualisation and simple classification
tutorial
4 Classification – part 1 Sup.L. Model quality evaluation tutorial Data preparation and visualization
test (1h) – immediately after the tutorial
5 Classification – part 2: algorithms
Homework 1 explanation
Classification tutorial
6 Classification part 3 and Regression Regression and time-series tutorial
7 Reading week -
8 Regression
Homework 2 explanation
Unsupervised learning tutorial Homework 1 deadline
9 Regression and Unsupervised learning Homework hour with Q&A
10 Recap and feature selection Anomaly detection tutorial
11 Data sharing, storage, and privacy Homework hour with Q&A Homework 2 deadline
12 Guest lecture Discussion
Introduction to Machine Learning – 2022-2023
Written test (2h)

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Evaluation
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• Laboratory test (10%)
• Individual homework (25%)
• Group assignment (25%)
• Written test (40%)
Theory
Technical skills
Communication
5 ECTS = 125h

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Useful references
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• Python tutorial
https://coherentpdf.com/python/pythonfromtheverybeginning.html
• Jupyter-notebook tutorial
https://www.dataquest.io/blog/jupyter-notebook-tutorial/
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019

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Introduction to ML
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- ML (what is it?
How does it work?
How do we use it?)
- Most common algorithms
- Challenges in the context of
smart-cities
Theory
Technical skills
Communication
- Introduction to computer programming
- Python
- Data visualization
- Data analysis
- ML
- The concept/algorithm matters more
than the syntax or issues with libraries!
- Expert – non-expert interaction
- Problem -> specify requirements ->
understand challenges -> interpret
results

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Introduction to ML
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Theory
Technical skills
Communication
- This module will give you the basics. A good start, but you won’t be ML experts (but you can get there!)
- You will learn how to communicate with ML experts
- It is essential that you play around with the code, try different variations on other datasets. Re-running our tutorials
won’t be enough
- Interact with us! Ask questions. Use the online resources. Don’t wait, start straight away!

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Introduction to ML
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- Issues with the code during the assessment/labs? No problem. Ask us questions or use pseudocode to describe
exactly what you wanted to do
Use the Internet!

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Introduction to ML
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- Let’s not reinvent the wheel!
- We will study what tools are available, how they work, and how to best use them!
- But not black-box style!

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Your Continuous Feedback is Important!
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Your Background and Prior Experience
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• Laptop/computer
• Excel
• OS (Operating System)
• Coding (Python)
• What’s a variable
• Integer, double, logical
• for loop, function, class?
• What’s a compiler?

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https://jupyter.readthedocs.io/en/latest/install/notebook-classic.html
Setting up your coding environment
Download and install Anaconda
- Windows or Mac OS: run Anaconda Navigator from
the Start menu or application menu
- In Linux: run anaconda-navigator from the terminal

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What is Artificial Intelligence (AI)?
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What is Artificial Intelligence (AI)?
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https://www.youtube.com/watch?v=760lA2YCKjM
https://www.youtube.com/watch?v=78-1MlkxyqI

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What is Artificial Intelligence (AI)?
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Weak AI
Strong AI
“Artificial intelligence: A modern approach”,
Russel and Norvig
• The study of intelligent agents
• Systems/devices that perceive their
environment and take actions in that
environment to achieve their goals
• Weak AI: “Intelligent” actions (like or better
than humans)
• Strong AI: “Intelligent” thinking

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What is machine learning (ML)?
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AI
ML
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
Learning can be seen as a process for improving performance
based on experience
To be defined
To be defined

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What is machine learning (ML)?
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Instead of trying to produce a programme to simulate the adult mind, why not
rather try to produce one which simulates the child's? [Alan Turing, 1950]

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What is machine learning (ML)?
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“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019

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What can we do with ML?
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“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
ML is generally about learning patterns in the data and use that information for our goals (e.g., spam filter)
How can we learn these patterns?
We don’t tell the algorithm how to detect spam emails.
We give the ML algorithm examples of spam emails. Then, it has to figure out how to detect them.

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What can we do with ML?
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Supervised learning
• Classification
• Regression
Unsupervised learning
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019

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Supervised Learning
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y = f(X)
f ynew
Model Training (learning or fit)
Xnew
f y
X
Using the model (test)
known known
unknown known
known unknown

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Supervised Learning
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Classification example
X (Data)
Y (Labels – desired output)
1 1 1 1 2 2 2 2 3 3 3 3
New unseen image
ML_model: y = f(X) ML
model
2
Good
prediction

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Unsupervised Learning
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Clustering example
X (Data)
ML
model

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What can we do with ML?
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“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
In the simplest case, the ML model learns just once. It will not learn later on or change automatically.
So, it learns once and then it is used as an anomaly detection tool.
X = [feature1, feature2, …, featurek]

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What can we do with ML?
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“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
Reinforcement learning
- Like humans, these algorithms
learn over time (one step at a
time)
- Learn from the outcome (e.g.,
“good” or “bad”) of your actions.
Learn from your successes and
mistakes

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What can we do with ML?
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Real life data are messy! Noisy data, missing data, artifacts, mislabelled data.
https://platerecognizer.com/blueiris/

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Objectives and learning outcomes
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• MLO1 Configure a programming environment suitable for exploring ML techniques
• MLO2 Prepare datasets for ML processing, visualise the data, and understand the consequences of decisions made in cleaning data
• MLO3 Assess the performance of a ML pipeline
• MLO4 Critically evaluate the outputs of a ML pipeline
• MLO5 Communicate with ML experts and non-experts: Explain goals and requirements of a project, interpret the outcomes of typical ML
analyses, present results to non-experts.
• MLO6 Assess the cost/benefit of distinct ML methodologies and explain what makes one approach more suitable than another one for a
given task
• MLO7. Understand challenges involving data sharing, storage, and privacy
Problem/question Data collection
Preprocessing /
cleaning
Analysing
Interpretation /
outcome
Improve
ML

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What can we do with ML?
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“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
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