Tutors
- Weihao (Patrick) Li
- Harriet Mason
- Brenwin Ang
- Brendi Ang
Consultations
For zoom consultations check Moodle for the links.
- Di Cook: Thu 10:30-11:30 on zoom
- Weihao (Patrick) Li: Tue 2:00-3:00 Menzies 1106
- Harriet Mason: Wed 11:30-12:30 on zoom
- Brenwin Ang: Thu 12:30-2:00 Menzies 1106
- Brendi Ang: Fri 10:00-11:30 on zoom
Assessments
Final exam 60%, project and assignments are each worth 9%, weekly quizzes 4%.
Tentative Schedule
See moodle for the zoom links.
- Lectures: Mon 4-6pm online with zoom
- Tutorials:
- ETC3250 A: Tue 10:30 CL_Anc-19.LTB_392 FF-Col (30) (Brendi) Extra details
- ETC3250 B: Tue 13:00 CL_Anc-19.LTB_392 FF-Col (30) (Brendi) Extra details
- ETC5250 C: Wed 08:00 CL_19 Ancora Imparo Way, Room G62 - LTB (Bldg 92) (Brenwin) Extra details
- ETC3250 D: Thu 9:00 LTB G56 (Brenwin) Extra details
- ETC3250 E: Thu 10:30 CL_33 Innovation Walk, Room JG03, Tutorial (Bldg 73P) (Brenwin) Extra details
- ETC3250 F: Thu 14:30 online with zoom (Harriet)
- ETC3250 G: Thu 16:00 online with zoom (Brendi) Extra details
- ETC5250 H: Fri 9:00 LTB G56 (Patrick)
- ETC5250 I: Fri 14:00 online with zoom (Harriet)
- ETC5250 J: Fri 15:30 online with zoom (Patrick)
There are no lectures, tutorials or consultations during the mid-semester break. You are expected to attend the lecture and ONE tutorial stream. Please see your personal timetable to determine which tutorial you are in. You can only attend the tutorial that you are enrolled in.
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Week
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Slides
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Tutorial
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Topic
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Readings
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Assessments
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1
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A: ; B:
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Introduction to statistical learning
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Chapter 2
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2
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A: ; B:
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Regression methods
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Chapters 3 and 7
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3
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A: ; B:
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Categorical response regression and resampling methods
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Chapters 4 and 5
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4
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A: ; B:
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Dimension reduction
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Chapters 4.4, 12.2 (optional: 6.3)
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Assignment 1 due
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5
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A: ; B:
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Visualising high dimensions
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Familiarise yourself with the R package, tourr, and read the material on high-d visualisation available on moodle
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6
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A: ; B:
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Classification and regression trees
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Chapter 8.1
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7
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A: ; B:
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Random forests, and support vector machines
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Chapter 8.2, and 9.1-9.3
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Assignment 2 due
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Midsemester Break (1 week)
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8
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A: ; B:
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Neural networks and regularisation
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Chapter 10.1-10.3, 10.7 + Chapter 6.1-6.2
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9
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A: ; B:
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Model assessment
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Chapter 9.6.3
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Assignment 3 due
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10
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A: ; B:
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Unsupervised classification
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Chapter 12.4.1-2
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11
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A: ; B:
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Unsupervised classification
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Chapter 22 of Hands-On Machine Learning with R
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Project due
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12
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Kaggle challenge discussion and presentations
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Expectations
- The computer software R and RStudio Desktop will be used for the unit. Please install the latest versions on your computer. If you need help on installation and basic R usage, got to https://learnr.numbat.space.
- Lectures will be delivered live on zoom, and you are expected to have either attended the lecture, or watched the recordings fully, prior to the tutorial for the week.
- Tutorials are not recorded, and attendance is expected.
- We recommend using your own laptop with the software installed for tutorials. Check the tutorial instructions ahead to get the list of software needed for that week. (The University may be able to provide you with a laptop if needed.)
- The moodle discussion forum is the appropriate place for questions and comments related to the course. Only questions of a personal issue or an administrative matter, will be responded to by email, and must be sent to ETC3250.Clayton-x@monash.edu. Email to any other address will be ignored. Note that, it is not acceptable to email your tutors.
- Structuring your questions carefully makes it easier to get help. If you have a question involving computing, follow the instructions for creating a reproducible example at How to ask for help on R. If it is about course material, point to the lecture slide or the textbook page.
- If you join the class after the first day of the semester or if you miss a lecture/tutorial, it is your responsibility to catch up with missed material, learn about due dates for material to be turned in.
- As a Monash unit, instructors and students agree to adhere to Assessment and Academic Integrity Policy. Students are responsible for their own good academic practice and must:
- undertake their studies and research responsibly and with honesty and integrity;
- credit the work of others and seek permission to use that work where required;
- not plagiarise, cheat or falsify their work;
- ensure that their work is not falsified;
- not resubmit any assessment they have previously submitted, without the permission of the chief examiner; appropriately acknowledge the work of others;
- take reasonable steps to ensure that other students are unable to copy or misuse their work; and
- be aware of and comply with University regulations, policies and procedures relating to academic integrity.
R package installation list
Install the latest versions of R and RStudio. Here is a partial list of R packages as of the start of semester. There may be more packages to install as the semester progresses. You should be able to install by cutting and pasting this code into your RStudio console.
install.packages(c("knitr", "tidyverse", "tidymodels", "gapminder", "gridExtra", "patchwork", "tourr", "kableExtra", "fpc", "statquotes", "yardstick", "ISLR", "broom", "mvtnorm", "ggdendro", "RColorBrewer", "plotly", "htmltools", "splines", "grid", "rpart", "mgcv", "gratia", "GGally", "ggpubr", "MASS", "modelr", "boot", "gganimate", "ggrepel", "ggthemes", "datasauRus", "geozoo", "ggparallel", "ggmosaic", "nullabor", "rpart.plot", "e1071", "viridis", "randomForest", "mlbench", "gtable", "keras", "nnet", "penalizedLDA", "ggExtra"))