Lecture 1: Introduction

DATA 311: Machine Learning

Dr. Irene Vrbik

University of British Columbia Okanagan

Welcome!

Welcome to DATA 311: Machine Learning

DATA_O 311 (3) Machine Learning

Regression, classification, resampling, model selection and validation, fundamental properties of matrices, dimension reduction, tree-based methods, unsupervised learning. [3-2-0]

Prerequisite: Either (a) one of STAT 205, STAT 230 or (b) a score more than 75% in one of APSC 254, BIOL 202, PSYO 373; and one of COSC 111, APSC 177.

https://docs.google.com/presentation/d/1EBTp6ruMp9X3XdhCFyFXM81_rFV_R55NBaMY0Pr5gSU/edit#slide=id.p notes on transition

STATS requirement:

• STAT 205: Introduction to Mathematical Statistics
• STAT 230: Introductory Statistics
• APSC 254 (3) Instrumentation and Data Analysis
• PSYO 373 (3) Advanced Research Methods and Statistics

Computer programming

• COSC 111 (3) Computer Programming I (Java?)
• APSC 177 (3) Engineering Computation and Instrumentation (Java?)

A little about me

• I am currently a Tenure-track Assistant Professor of Teaching

• I have taught a variety of courses (from introductory data science and to graduate courses in statistics) at several institutions (Guelph, McGill, MDS Program)

• I am currently the Data Science Program advisor, Articulation, and curriculum representative

Where can you find me?

Office: SCI 104 email: irene.vrbik@ubc.ca

Websites: irene.vrbik.ok.ubc.ca

Educational Background

1. McMaster University, BSc (Mathematics & Statistics)

2. University of Guelph, MSc (Applied Statistics)

   Thesis: Using Individual-level Models to Model Spatio-temporal Combustion Dynamics. This involved modelling the spatio-temporal combustion dynamics of fire in a Bayesian framework. Supervisors: Rob Deardon and Zeng Feng.

3. University of Guelph, PhD (Applied Statistics)

   Thesis: Non-Elliptical and Fractionally-Supervised Classification. This involved model-based classification with a particular emphasis on non-elliptical distributions. Supervisor: Paul D. McNicholas.

Experience

Postdoctoral Fellow at McGill University Under the supervision of Dr. David Stephens, this work focused on the statistical and computational challenges associated with analyzing genetic data. It involved clustering and modeling HIV DNA sequences.

Postdoctoral Fellow at UBCO Awarded by NSERC (Natural Sciences and Engineering Research Council of Canada), this research involved collaborations with faculty from several disciplines (eg. Medical Physics, Biology, and Chemistry) and was supervised by Dr. Jason Loeppky.

Instructor at UBCO a three-year contract position in the Department of Computer Science, Mathematics, Physics, and Statistics.

NSERC = is the major federal agency responsible for funding natural sciences and engineering research in Canada.

Research Interests

Statistics and Machine Leaning in Curriculum Design

• e.g. topics modeling in Data Science course calendars

Curricular Analytics the systematic analysis and evaluation of educational curricula to gain insights into various aspects of curriculum design, delivery, and assessment.

• e.g. metric calculation for various pathways, curriculum visualization, course recommendation systems

Tools for teaching, learning, and technology

• e.g. Prairie Learn: online problem-driven learning system for creating homework and tests

Topic Modeling Leverage a class of Bayesian models (Latent Dirichlet Allocation (LDA)) to model pathways through Data Science degrees. Aim to cluster words into latent topics and discover common learning outcomes in Data Science Curricula.

Curricular Analytics Use this information to improve curricula and improve their effectiveness and impact on student learning outcomes. Visualization of curricula as graphs. Metric calculations

Others: tools for teaching, learning, and technology

Course Syllabus

The course syllabus is a dynamic document which has been posted to Canvas and course website. Many administrative questions can be answered there.

Course Tools

Canvas will be use for most course related material:

• Grades
• Assignments (downloading/submitting)
• Course announcements/discussions
• Supplementary files (eg. data sets, code, etc…)

Lectures

• Lectures will be posted on our course webpage.

• Take time to learn how to:

  • navigate through the slides

  • export to PDF (good for tablet annotation)

  • use the clipboard (example Clipboard code HTML only)

Some of this material is repeated from the Course outline but I wanted to give you a sense of what a lecture would look like. I am playing around with different themes so if you hate the inverse slides (dark background) for printing reasons, just let me know and I’ll stick to white

For more information: https://quarto.org/docs/presentations/revealjs/presenting.html

Programming Language

• Any necessary coding will be done in R:
• Relevant code will be posted to Supplmentary Files Canvas when necessary. Most relevant pieces will be embedded in the slides and/or included in Labs
• It is also recommended that you complete assignments using Rmarkdown in RStudio.

Clipboard code

How to use the clipboard

Hover over the code block below and you will see a copy icon in the top-right corner. This will copy the code to your clipboard for easy pasting into your R session. ⚠️ This feature only works in HTML output

x <- c(2, 4, 6, 8)
mean(x)

[1] 5

x + 2

[1]  4  6  8 10

Paged Data Frames

iris

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Warning

This interactivity will only work on the HTML version of the slides, not PDF.

• If you have a laptop, code along side me if you want!
• Most R content will be discuss in labs, however.
• If viewing in HTML format, you can hover over the code (AKA “R chunk”) for easy copying/pasting.

Why R?

Pros

• exposure to R in Statistics prerequisite course
• Rich Ecosystem
• Reproducibility
• Textbook

Cons

• Steep learning curve
• Performance
• Package Quality
• Limited Industry Adoption

• R was developed by statisticians, so it has a strong foundation in statistical modeling and analysis.
• R has a vast and comprehensive ecosystem of packages and libraries specifically designed for data analysis, visualization, and statistics.
• While R’s primary strength is statistics, it also offers a growing number of machine learning packages, such as caret, randomForest, and xgboost, making it suitable for a variety of data science tasks.

While R is a powerful language for data science, it also has some limitations and drawbacks that you should consider before choosing it for your data science projects. Here are some of the cons of using R for data science:

CONS :

Learning Curve: R has a steeper learning curve compared to some other languages like Python, especially for those who don’t have a strong background in statistics. The syntax can be less intuitive for beginners.

Performance: R can be slower than languages like C++ or Python, particularly when dealing with large datasets or computationally intensive tasks. While there are ways to optimize R code, it may not be the best choice for high-performance computing.

Package Quality: While R has a rich ecosystem of packages, the quality and documentation of packages can vary widely. Some packages may be poorly maintained or lack support, which can lead to compatibility issues and frustration.

Limited Industry Adoption: While R is widely used in academia and certain industries like finance and healthcare, it may not be as prevalent in other sectors. This could impact job opportunities and collaboration with colleagues who use different tools.

Lab Delivery

• Labs will be held in person; students must be enrolled in a lab (which cannot conflict with other courses)

• TAs provide guidance on carrying out analyses in R for the techniques discussed in lecture.

• Knowledge of commands and programming techniques will be evaluated throughout the course.

• Follow the instructions carefully and practice skills by completing (and redoing!) labs and assignments

• If you prefer to work through the lab on your own time you are welcome to do so
• By doing so, you will however, miss out on TA support
• A lot of the code you need for doing assignments will be presented in lab

Textbook

The main textbook reference for this course is:

ISLR: An Introduction to Statistical Learning with Applications in R (Second Edition). By: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.

This book is available for free at statlearning.com; see resources here.

A secondary (less referenced) textbook is:

ESL: The Elements of Statistical Learning: data mining, inference, and prediction, 2nd edition. By: Hastie, Tibshirani, Friedman.

Can be downloaded for free at: hastie.su.domains/ElemStatLearn.

• Note the acronyms as I will be using them through the course.

• Sections of these books will be presented as suggested (i.e. optional) readings throughout the course

• ISLR is a bit of a lighter read

• These will be a great source for supplementary examples and explanations

Lecture format

• Slides will occasionally be supplemented with handwritten material.
• Aside for doodling, substantial written material will be done digitally (on my iPad) and uploaded to Canvas.
• Lectures may also include discussions which you will only gain access to by attending class.
• You will not get the whole story by reading the slides!

Class Etiquette

1. Please be respectful, especially to other students
2. Please be present. Attendance will not be taken, but you are encouraged to come and learn together.
3. Please restrict the use of electronic devices to course related material; other content could be distracting.
4. Please be forgiving; instructors are people too, we will make mistakes.

Course Questions

In class

• If you are stuck on a concept during lecture, please feel free to raise your hand and ask for clarification.
• If you are needing help understanding something, chances are, other students are too!
• I will do my best to answer questions on the fly or organize a more thoughtful answer to be presented first thing next class or posted to Canvas.

Course Questions

Outside of class

Outside of class, the general order in which I would suggest you asking course-related questions is:

1. Consult the course syllabus
2. Post your question on the public forum on Canvas*
3. Come see me during student hours or visit your TA during lab (whichever comes first)
4. e-mail (weekdays are best)

Machine Learning

An algorithm is a step-by-step set of instructions for solving a problem or accomplishing a task.

Model: In general, any mathematical construct that processes input data and returns output. Phrased differently, a model is the set of parameters and structure needed for a system to make predictions. In supervised machine learning, a model takes an example as input and infers a prediction as output. Within supervised machine learning, models differ somewhat.

Machine learning (ML) is a subfield of artificial intelligence (AI) that uses algorithms and statistical models to learn from data to perform complex tasks.

• e.g. recommend TV shows you might like, determine if an e-mail is spam or not, predict the selling price of a home.

• AI is computer software that mimics the ways that humans think in order to perform complex tasks, such as analyzing, reasoning, and learning

• most AI is performed using machine learning, so the two terms are often used interchangeably

  Very wide range of applications:

  • from healthcare (diagnosis, personalized medicine)

  • to finance (fraud detection in credit cards)

  • e-commerce (recommendation systems - Amazon suggesting products you might like)

  • marketing (targeted advertising)

  • autonomous driving (deep learning)

  • natural language processing (NLP) models used in chatGPT

How does Machine Learning work

Machine Learning has been described¹ as:

“The field of study that makes computers capable of learning without being explicitly programmed.”

1. attributed to Arthur Lee Samuel, an early American leader in AI, who also happened to coin the term “Machine Learning” in 1959 while at IBM.

Cats

Dogs

I might give you a bunch of labeled images of cats and dogs. By examining many examples, we learn to tell these two types of species apart so that when a new image comes along that we may not necessarily have seen before, we are able to give a pretty good guess as to whether it is a cat or a dog.

Traditional Programing

Input:

e.g. If claws are sharp and nose is small …

Output: cat

• Notice that a human is explicitly programming these rules.
• requires “expertise” in pets
• The machine executes the program (that this human has developed) and generates output following some logical statements (ie computer code).
• As the system grows more complex, more rules are needed.

Machine Learning

In supervised machine learning the computer must learn these distinguishing patterns for itself in order to determine a set of rules by which future data will be sorted.

Rather than developing these rules, we feed data and they build their own logic based on patterns

• Supervised problem we give the pictures and labels
• learns how the input and output data are correlated and use that to create a rule for classifying new data

powerful: a lot easier to give examples than to write code

• it adaptable to new and unseen scenarios without needing to be explicitly reprogrammed for each case
  • e.g might be good at classifying a tabby cat as a cat even though it has never seen an example

General concepts

• To continue with the cats and dogs, the more examples a human is given, the better they would become at distinguishing between the two species.

• Generally speaking, the more data a computer receives the more fine-tuned the algorithms get and the more accurate it can be in its predictions

• The more samples you have, the more chances you have of including all dog/cats species and “teach” your program

• up to a point.. too much data is a bad thing (overfitting)

• The more variety in the samples, the easier it may become in detecting patterns and ultimately predicting the result.

• e.g. given more data a computer might discover that dogs come in a larger variety of sizes than do cats, and use this information to devise a new rule.

• e.g. if we only give exampls of Sphynx cats (which are hairless) we might get the false impressions that all cats don’t have hair

• up to a point.. too much data is a bad thing (overfitting). plateau

• This process is often iterative.

• In the same way a human brain uses experience to improve on a learned task, a computer may improve in efficacy over time in response to new data.

• Iteration allows for incremental improvements in the model (tuning), leading to better performance and more robust outcomes.

• The main take-a-way is that it is the computer, not the programmer that identifies these patterns and establish the rules

Why is ML important?

• Many of the statistical techniques you’ve (probably) learned thus far are either completely inapplicable to much of this data, or only applicable on a subset.

• BIG data often goes hand-in-hand with machine learning (thought not necessarily)

• With growing access to data and computing power, models can be built faster than ever and used in countless fields to gain useful insights.

• Government and industry are collecting more data with increased complexity.

• This course will guide you through a few of the classical approaches in Machine Learning (ML)

Supervised ML

Involves training a model on a labeled examples. There are two main goals:

• Classification: Assigning unseen examples into to predefined categories (e.g., spam vs. not spam).

• Regression: Predicting continuous values (e.g., predicting house prices based on features).

Unsupervised ML

Involves training a model on unlabeled examples.

• Clustering: Grouping similar data points into clusters (e.g., customer segmentation).
• Dimensionality Reduction: Reducing the number of features while retaining the most important information (e.g., reduce image size).

Supervised:

• where each input has a corresponding output label.

• is to make accurate predictions or classifications on new, unseen data by learning from the provided examples.

• learn a mapping from inputs to outputs based on the training data, so the model can predict the labels for unseen data accurately.

Unsupervised

• no “answers” a dataset without labeled responses

• Unsupervised model tries to learn the underlying patterns or structure from the data itself.

• eg. clustering (eg. customer segmentation),

• e.g. dimensionality reduction (eg. image compression - Reducing the size of high-resolution images for storage and transmission without losing significant details.

• also Association: Discovering rules that describe large portions of the data (e.g., market basket analysis) (not covered)

We will manly stay in the prediction landscape, but there many other examples of ML:

• e.g. Google photos face/voice recognition, sentiment analysis, recommendation systems, self-driving cars …

• medical diagnostics (detect tumors in x-rays)

• sentiment analysis this is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral eg. is a tweet positive, neutral, or negative

• cars: allow a car to collect data on its surroundings from cameras and other sensors, interpret it, and decide what actions to take.

Classical learning can be categorized into supervised (“answer” so to speak) vs. unsupervised (labeled data).

• Unsupervised model tries to learn the underlying patterns or structure from the data itself.

  • eg. clustering (eg. customer segmentation), dimensionality reduction (eg. image compression - Reducing the size of high-resolution images for storage and transmission without losing significant details.

• Supervised goal is to learn a mapping from inputs to outputs based on the training data, so the model can predict the labels for unseen data accurately.

  • Regression: used to predict continuous, numerical values (eg. house pricing)
  • Classification: used to predict discrete classes or categories (eg. spam detetection)

For each model you will need to:

• understand the idea behind them
• what are the advantages and disadvantages
• whether they can be applied for unsupervised versus supervised problems
• if the latter, are they regression or classification or problems
• need to know the
  • basics of training a machine learning model (both code and concepts)
  • training vs testing: the process behind splitting data into training/validating and reserving some data for testing your machine learning algorithm
  • resampling techniques (including bootstrapping and cross-validation)
  • different performance metrics for assessing models (eg. F1 score, precision/recall for classification), (eg, MSE for regression)
  • bias vs

Discriminant analysis is a statistical technique used for classification — that is, assigning observations to predefined groups based on their characteristics (predictor variables).

Discriminant analysis finds a boundary (or rule) that best separates those groups.

might have seen this in DATA 101

very simple and fast and used a lot as a starting point for that reason

Resample techniques: Cross-validation and boostrap:

• key for model validation
• tuning parameters
• also for emsemble methods

Bootstrap and CV aren’t ensemble methods themselves, but they’re the resampling tools that make certain ensembles work.

Ensemble methods are not a “type” of learning in themselves

Ensemble methods are strategies for combining models.

• bagging/RF - built on the bootstrap
• boosting neither bootstrap nor CV is at its core but releated concepts

this is NOT a course in deep learning

exactly one lecture on Neural nets

Many datasets have lots of variables (features), which can make models harder to interpret, slower to run, and more prone to overfitting.

Dimensionality reduction techniques aim to represent the data with fewer variables while keeping as much information (structure/variance) as possible.

PCA (Principal Component Analysis)

• Finds new variables (principal components) that are linear combinations of the originals. These components capture the maximum variance in the data.
• Often used for visualization (plotting data in 2D/3D) or preprocessing before supervised learning.

FA (Factor Analysis)

• Similar in form to PCA but with a different goal: explains correlations between variables using a smaller set of latent factors.
• More focused on uncovering hidden constructs (e.g., “math ability” underlying several test scores).

NMF (Non-negative Matrix Factorization)

• Decomposes a data matrix into non-negative parts, making results easier to interpret.
• Common in text mining (e.g., extracting topics from document–term matrices).

Imagine you have a giant table of numbers (like word counts in documents, or pixel brightness in images). It’s messy and hard to interpret. NMF says: “Let’s break this big table into two smaller, simpler tables that, when multiplied together, approximate the original — but only using positive numbers.”

what this course is not heavily focused on:

• deep learning (just one lecture)
• Natural Language processing (none - how chatGPT works)
• image processing (application to images in PCA but not our focus)
• exit if this is not for you as there is a waitlist

iClicker

Students are expected to participate in iClicker questions by enrolling at: https://join.iclicker.com/YUHA

https://lthub.ubc.ca/guides/iclicker-cloud-student-guide/

iClicker Example

iClicker Test Question (Not for Marks)

How are you feeling about your first semester so far?

1. 🎉 Excited and ready

2. 😅 A little overwhelmed

3. 🤔 Still figuring things out

4. ☕ Ask me after more coffee

If you cannot answer this question please follow the iClicker Cloud Student Guide: https://lthub.ubc.ca/guides/iclicker-cloud-student-guide/