4.1a-ML-Introduction.Rmd

---
title: 'Targeted Metabolomics Data Analysis: Unlocking Insights with Machine Learning, AI and Statistics'
subtitle: 'Day 3 – Lecture 1a-Introduction to Machine Learning'
author: "June 11-14, 2024"
institute: "Barcelona, Spain"
date: ""
output:
  xaringan::moon_reader:
    css: [default, metropolis, metropolis-fonts, "mycss.css"]
    lib_dir: libs
    nature:
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editor_options: 
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---

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```{r setup, include=FALSE}
options(htmltools.dir.version = FALSE, echo=FALSE,
        message=FALSE,warning=FALSE,
        fig.dim=c(4.8, 4.5), fig.retina=2, out.width="100%")

knitr::opts_chunk$set(echo = FALSE)

knitr::knit_hooks$set(mysize = function(before, options, envir) {
  if (before) 
    return(options$size)
})
```

# Outline

.columnwide[
  ### 1) Introduction
  ### 2) Statistical Learning
  ### 3) Overfitting
  ### 4) Evaluating Machine Learning Models
  ### 5) The workflow of Machine Learning
  ### 7) References and Resources
]

---

class: inverse, middle, center

name: Introduction

# Introduction and motivation

---

# Machine Learning, A new field?

.center[
## *Machine Learning is The field of study that gives computers the ability to learn without being explicitly programmed.*”

Arthur Samuel (1956)
]

```{r out.width="80%", fig.cap='From spectra and images to data tables'}
knitr::include_graphics("3.1-StatisticsBackground_insertimage_1.png")
```

---

# ML vs traditional programming

```{r out.width="80%", fig.cap='From spectra and images to data tables'}
knitr::include_graphics("3.1-StatisticsBackground_insertimage_2.png")
```

---


# ML vs traditional programming (ctd.)

```{r out.width="80%", fig.cap='From spectra and images to data tables'}
knitr::include_graphics("3.1-StatisticsBackground_insertimage_3.png")
```

<!-- --- -->

<!-- # ML vs conventional programming -->

<!-- -	Conventional computer programs or algorithms perform tedious tasks faster and more accurately than humans -->

<!--   - Tasks Example: *Addition, subtraction, spell-checking* -->

<!-- -	Machine learning algorithms perform tasks that are difficult or infeasible to do via conventional computer algorithms  -->

<!--   - Tasks Example: *Grammar checking, interpreting speech, image recognition* -->


---


<!-- # Learning vs Machine Learning -->

<!-- - **Learning** -->

<!--   - Any process by which an organism or system improves performance from experience. -->

<!-- - **Machine Learning** -->

<!--   –	A branch of artificial intelligence (AI) in which a computer automatically improves performance from experience. -->

<!--   - A technique to develop programs that can make predictions or decisions without being explicitly programmed to do so -->

<!-- --- -->



---


# A Machine Learning Workflow

```{r out.width="80%", fig.cap='', fig.align='center'}
knitr::include_graphics("3.1-StatisticsBackground_insertimage_4.png")
```


---


# Another ML Workflow

```{r out.width="80%", fig.cap='', fig.align='center'}
knitr::include_graphics("3.1-StatisticsBackground_insertimage_5.png")
```


---


# Contruct your dataset
![Plot title. ](3.1-StatisticsBackground_insertimage_6.png)


# Data Transformation
![Plot title. ](3.1-StatisticsBackground_insertimage_7.png)

# Feature Engineering


# Scaling, Normalizing Standardizing


```{r out.width="80%", fig.cap='', fig.align='center'}
knitr::include_graphics("")
```

---


# 

```{r out.width="80%", fig.cap='', fig.align='center'}
knitr::include_graphics("")
```

---


# 

```{r out.width="80%", fig.cap='', fig.align='center'}
knitr::include_graphics("")
```






---

class: inverse, middle, center

name: Resources

# References and Resources

---