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index.qmd
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---
title: "ETC3250/5250 Introduction to Machine Learning"
---
```{r}
#| label: load_packages
#| include: false
#| message: false
#| warning: false
#| echo: false
#| cache: false
library(tidyverse)
options(knitr.kable.NA = '')
source(here::here("course_info.R"))
week <- as.integer(1 + ((Sys.Date() - as_date(start_semester))/7))
```
## Lecturer/Chief Examiner
* **Professor Di Cook**
- Email: [[email protected]](mailto:[email protected])
- Consultation: Thu 9:00-10:30 (zoom only)
## Tutors
* Patrick Li
- Tutorials: Mon 15:00 (LTB 323), Fri 11:00 (CL_33 Innovation Walk, FG04 Bldg 73P)
- Consultation: Thu 10:30-12:00 (W9.20)
* Harriet Mason
- Tutorials: Wed 18:00 (LTB G60), Fri 12:30 (CL_33 Innovation Walk, FG04 Bldg 73P)
- Consultation: Thu 3:00-4:30 (zoom only)
* Jayani Lakshika
- Tutorials: Wed 8:00, 9:30 (CL_33 Innovation Walk, FG04 Bldg 73P)
- Consultation: Thu 12:00-1:30 (W9.20)
* Krisanat Anukarnsakulchularp
- Tutorials: Mon 12:00, 13:30 (LTB 323)
- Consultation: Fri 9:30-11:00 (W9.20)
## Weekly schedule
* Lecture: Wed 1:05-2:45pm
* Tutorial: 1.5 hours
* Weekly learning quizzes due Mondays 9am
```{r}
#| label: schedule2
#| message: false
#| warning: false
#| echo: false
#| output: asis
schedule |>
transmute(
Show = !is.na(Week), #& (Week <= week | Week <= 1),
Topic = if_else(!Show, Topic, glue::glue("[{Topic}](./week{Week}/index.html)")),
Reference = if_else(is.na(Week), Reference, glue::glue("[{Reference}]({Reference_URL})")),
Assessments = if_else(is.na(Assignment), Assignment, glue::glue("[{Assignment}]({File})")),
Week = format(Date, "%d %b")
) |>
select(-Show) |>
select(Week, everything()) |>
knitr::kable(format = "markdown")
```
## Assessments
* [Weekly learning quizzes](https://learning.monash.edu/course/view.php?id=9453§ion=20): 3%
* Assignment 1: [Instructions](assignments/assign01.zip), [Submit to moodle](https://learning.monash.edu/course/view.php?id=9453§ion=20) (9%)
* Assignment 2: [Instructions](assignments/assign02.zip), [Submit to moodle](https://learning.monash.edu/course/view.php?id=9453§ion=20) (9%)
* Assignment 3: [Instructions](assignments/assign03.zip), [Submit to moodle](https://learning.monash.edu/course/view.php?id=9453§ion=20) (9%)
* Project: [Instructions](project/project.zip), [Submit predictions to kaggle](https://www.kaggle.com/competitions/maji-safi), [Submit files to moodle](https://learning.monash.edu/course/view.php?id=9453§ion=20): 10%
* Final exam: 60%
## Software
We will be using the latest versions of [R](https://cran.rstudio.com) and [RStudio](https://posit.co/download/rstudio-desktop/).
Here is the code to install (most of) the R packages we will be using in this unit.
```r
install.packages(c("tidyverse", "tidymodels", "tourr", "geozoo", "mulgar", "ggpcp", "plotly", "detourr", "langevitour", "ggbeeswarm", "MASS", "GGally", "ISLR", "mvtnorm", "rpart", "rpart.plot", "randomForest", "e1071", "xgboost", "Rtsne", "classifly", "penalizedLDA", "nnet", "kernelshap", "shapviz", "iml", "DALEX", "cxhull", "fpc", "mclust", "ggdendro", "kohonen", "aweSOM", "patchwork", "ggthemes", "colorspace", "palmerpenguins"), dependencies = TRUE)
```
If you run into problems completing the full install, the likely culprits are [tidyverse](https://www.tidyverse.org) and [tidymodels](https://www.tidymodels.org). These are bundles of packages, and might fail at individual packages. To resolve the problems, install each package from the bundle individually, and don't install any that fail on your system.
In addition, follow [these instructions](https://tensorflow.rstudio.com/install/) to set up tensorflow and keras, which requires having python installed.
If you are relatively new to R, working through the materials at [https://learnr.numbat.space](https://learnr.numbat.space) is an excellent way to up-skill. You are epsecially encouraged to work through Chapter 3, on Troubleshooting and asking for help, because at some point you will need help with your coding, and how you go about this matters and impacts the ability of others to help you.
The [ISLR](https://www.statlearning.com) book also comes with python code, and you are welcome to do most of your work with python instead of R. However, what you submit for marking must be done with R.