Welcome to CS909/CS429 Data Mining in Term-2 of 2025! This webpage is the primary source of information for all updates, announcements and content for this module.

Announcements

We will be adding major announcements for the module below.

• Students who might need to change group (due to mitigating circumstances or a timetable clash) should email queries to the relevant resource email account (DCS.UG.Support@warwick.ac.uk / DCS.PGT.Support@warwick.ac.uk).
• You are recommended to keep yourself updated through https://warwick.ac.uk/coronavirus/ as well as the student newsletter.Link opens in a new window

Module Teaching Team

Instructor: Fayyaz MinhasLink opens in a new window

Teaching Fellow: Ayşe SunarLink opens in a new window

Teaching Assistants

Name	Email
George Wright	george.wright.1@warwick.ac.uk
Piotr Keller	piotr.keller@warwick.ac.uk
Jack Bacon	jack.bacon@warwick.ac.uk
Boyang Fu	Boyang.Fu@warwick.ac.uk
Xiaoqing Fan	Xiaoqing.Fan@warwick.ac.uk
Xuefeng Xu	Xuefeng.Xu@warwick.ac.uk

Communication between students and teaching team

Please use moodle for non-urgent communication. It is very difficult to monitor and respond to emails from individual students due to the large size of the class.

For questions about Logistics or other issues, the first point of contact is Ayşe Sunar.

Instructor Office HoursLink opens in a new window

Moodle & Other Links

We shall be using this Moodle page for discussion and Q/A in the module.Link opens in a new window

Students can Post your QuestionsLink opens in a new window to the Moodle forum.

A series of Self Assessment QuestionsLink opens in a new window that you can answer for self-assessment and feedback are also available. As the goal of these questions is to encourage students to explore and self study, answers to all of these questions will not be provided but students are welcome to discuss them with the instructors in lab sessions or as questions in lectures or via Moodle.

Lecture CaptureLink opens in a new window is also accessible via Moodle.

Timetable

• All Times are UK Times

Lectures - Weeks 1–10

Time	Location
Monday 11:00 - 12:00	R0.21
Thursday 12:00 - 13:00	L3
Friday 13:00 - 14:00	OC1.05

Lab Sessions - Weeks 1–10

Each student has been allocated a lab session. Please check your scheduled lab session on Tabula and attend that. Please make sure that you attend the whole of an assigned lab session so that you do not miss attendance.

TA allocation to labs may change from week to week due to scheduling constraints. However, there should be at least one TA common between sessions.

NOTE: The following information may change if there are changes to face-to-face teaching.

Students who might need to change group (due to mitigating circumstances or a timetable clash) should email queries to the relevant resource email account (DCS.UG.Support@warwick.ac.uk / DCS.PGT.Support@warwick.ac.uk). The teaching staff wil only be able to sign-post to support teams.

Lab	Time	Location	TA Allocation (Week 1)	Teams Link
Lab 1	Thursday 10:00 - 11:00	CS0.01	George & Xiaoqing	N/A
Lab 2	Thursday 10:00 - 11:00	CS0.06	Jack & Xuefeng	N/A
Lab 3	Thursday 11:00 - 12:00	CS0.01	George & Boyang	N/A
Lab 4	Thursday 11:00 - 12:00	CS0.06	Piotr & Xuefeng	N/A
Lab 5	Friday 10:00 - 11:00	CS0.01	Jack & Xiaoqing	N/A
Lab 6	Friday 10:00-11:00	MB3.17	Piotr & Boyang	N/A

Coursework Assignments

• Assignment-1 (25% of final mark)
  Assignment Announcement: Week-2 (W/C Jan 13 2025): Assignment-1 Announced
  Assignment due: 12 noon Wed 12ᵗʰ February 2025
  Feedback due: Wed 12ᵗʰ March 2025
  Tabula Submission Links: CS429 CS909
  
• Assignment-2 (MEng: 25% of final mark, MSc: 35% of final mark)
  Assignment Announcement: Week-6 (W/C Feb 10 2025): Link to assignment will be available soon.
  Assignment due: 12 noon Mon 17ᵗʰ March 2025
  Feedback due: Wed 16ᵗʰ April 2025
  Tabula Submission Link for Student will be available soon.
  
• There will be a final exam in the module. Please check exam deadlines directly with Warwick Student Administrative ServicesLink opens in a new window

Books and Other resources

[PML-1] Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy. MIT Press, 2021. link: https://probml.github.io/pml-book/book1.html 

[PML-2] Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy. MIT Press, 2023. link: https://probml.github.io/pml-book/book2.html 

[IML] Introduction to Machine Learning 3e by Ethem Alpaydin (selected chapters: ch. 1,2,6,7,9,10,11,12,13)

[DBB] Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville, (Ch 1-5 if needed as basics), Ch. 6,7,8,9 link: https://www.deeplearningbook.org/Link opens in a new window

[FNN] Fundamentals of Neural Networks : Architectures, Algorithms And Applications by Laurene Fausett, (ch. 2,6)

[SODL] The Science of Deep Learning by Iddo Drori link: https://www.thescienceofdeeplearning.org/

Reading listLink opens in a new window

Casual Reading:

1. The master algorithm
2. The Alignment Problem
3. The book of why

Course Materials

Slides and reading materials will be posted each week. The lab session will be available prior to the start of the lab session. Please see the Lab Access section below for guidance on running the lab materials and Python guides.

NOTE:

• It is strongly recommended that you attend all lectures in person as this year's lectures would be significantly different in content and delivery from previous ones and we cannot guarantee the sufficiency of archived content for effective learning in terms of success in coursework or examination. However, if you are unable to attend lectures due to a genuine issue, you can use the links to archived lecture recordings from previous years which are available at this Course Stream ChannelLink opens in a new window as well as on YoutubeLink opens in a new window (https://bit.ly/2RannLB )

Week	Lectures	Reading/Resources	Labs
1 Jan 6	IntroductionLink opens in a new window Why Data Science?Link opens in a new window ApplicationsLink opens in a new window Research ApplicationsLink opens in a new window FrameworkLink opens in a new window SurveyLink opens in a new window Philosophy of MLLink opens in a new window Classification and Linear DiscriminantsLink opens in a new window Determining Linear SeparabilityLink opens in a new window Prelim: Gradients and Gradient descent Prelim: Gradient Descent CodeLink opens in a new window Prelim: ConvexityLink opens in a new window Perceptron ModelingLink opens in a new window Perceptron CodeLink opens in a new window	Introduction SlidesLink opens in a new window Applications and Framework SlidesLink opens in a new window k-Nearest Neighbor AlgorithmLink opens in a new window [Required] [PML] Chapter-1, [IML] Chapter-1 CRISPR Talk (optional)Link opens in a new window Whole Slide Images are Graphs Talk (optional)Link opens in a new window A few useful things to know about machine learning (recommended)Link opens in a new window Linear Discriminants (notes)Link opens in a new window Preliminaries (notes)Link opens in a new window Building Linear Models (notes)Link opens in a new window Gradient Descent Code (py)Link opens in a new window Perceptron Code (py)Link opens in a new window Perceptron AlgorithmLink opens in a new window Perceptron Classification VideosLink opens in a new window	Learning Python Implementing kNN classifierLink opens in a new window Gradient Descent and PerceptronLink opens in a new window k-Nearest Neighbor DemoLink opens in a new window Understanding lines Distance and Dot productLink opens in a new window Gradient Descent Code (py)Link opens in a new window Perceptron Code (py)Link opens in a new window
2 Jan 13	What's special in an SVM?Link opens in a new window SVM FormulationLink opens in a new window A brief history of SVMsLink opens in a new window Coding an SVM and CLink opens in a new window Margin and RegularizationLink opens in a new window Linear Discriminants and Selenite CrystalsLink opens in a new window Selenite Crystals bend SpaceLink opens in a new window Using transformations to foldLink opens in a new window Transformations change distance and dot productsLink opens in a new window Kernelized SVMs	Perceptron Notes (Updated 13 Jan 2024)Link opens in a new window SVM Notes (Updated 13 Jan 2024) Fold and Cut TheoreomLink opens in a new window Book Reading [SVM in PML, SVM in IML] SVM TutorialLink opens in a new window	k-Nearest Neighbor DemoLink opens in a new window Understanding lines Distance and Dot productLink opens in a new window Gradient Descent and PerceptronLink opens in a new window Gradient Descent Code (py)Link opens in a new window Perceptron Code (py)Link opens in a new window Linear Discriminant for AND Classifcation ProblemLink opens in a new window Regularized PerceptronLink opens in a new window Transformations codeLink opens in a new window Kernelized PredictorLink opens in a new window SVM AppletLink opens in a new window SVMLink opens in a new window Assignment-1 Announced
3 Jan 20	Revision of SVMs Scientific MethodLink opens in a new window Why measure performance?Link opens in a new window Accuracy and its assumptionsLink opens in a new window Confusion matrix and associated metricsLink opens in a new window ROC CurvesLink opens in a new window PR CurvesLink opens in a new window PR-ROC Relationship and codingLink opens in a new window Estimating GeneralizationLink opens in a new window (with CV, bootstrap estimation and statistical significance) CV in sklearnLink opens in a new window 4.10 Using MLXTend Twelve ways to fool the massesLink opens in a new window	SVM NotesLink opens in a new window HistoKernel Paper (optional read) HistoKernel Demo Quantum Kernel SVM for cancer classificationLink opens in a new window (optional read) Chapter 19 “Design and Analysis of Machine Learning Experiments” Alpaydin, Ethem. 2010. Introduction to Machine Learning. Cambridge, Mass.: MIT Press. Performance assessment Lecture NotesLink opens in a new window Performance Assessment ExerciseLink opens in a new window Ten ways to fool the masses with machine learningLink opens in a new window MLXTENDLink opens in a new window Optional Reading Wainberg, Michael, Babak Alipanahi, and Brendan J. Frey. “Are Random Forests Truly the Best Classifiers?” Journal of Machine Learning Research 17, no. 110 (2016): 1–5. Munir, Farzeen, Sadaf Gull, Amina Asif, and Fayyaz Ul Amir Afsar Minhas. “MILAMP: Multiple Instance Prediction of Amyloid Proteins.” IEEE/ACM Transactions on Computational Biology and Bioinformatics, August 22, 2019. https://doi.org/10.1109/TCBB.2019.2936846.	SVMLink opens in a new window Performance Assessment ExerciseLink opens in a new window Performance Assessment ExerciseLink opens in a new window Work on Assignment-1
4 Jan 27
5 Feb 3
6 Feb 10
7 Feb 17
8 Feb 24
9 Mar 3
10 Mar 10
REVISION TBA

Lab Access and Machine Requirements

Remote Machine Login: https://warwick.ac.uk/fac/sci/dcs/intranet/user_guide/remote-login/

Use "module load cs909-python". The following guide will show you how to run jupyter-notebook as well as loading using notebooks with the module loaded in Visual Studio Code:

Notebook and VSCode Setup GuideLink opens in a new window

If using your own machine, you will be needing the listed libraries.

1. Anaconda Python (3.6+)
2. Jupyter Notebook or Jupyter Lab
3. Matplotlib
4. Numpy
5. Scipy
6. Pandas
7. Scikit-learn
8. Keras, PyTorch and TensorFlow (with GPU configuration if GPUs available)

Learning Python

The following resources may be useful when familiarising yourself with Python.

Python Documentation: https://docs.python.org/3/tutorial/index.html

NumPy Website: https://numpy.org

Matplotlib Website: https://matplotlib.org

Video Tutorials

Courtesy of Dr. Greg WatsonLink opens in a new window

1. Introduction
2. Basic Variables
3. Lists
4. Control Flow
5. Functions
6. Tuples
7. Sets
8. Dictionaries
9. NumPy
10. Matplotlib
11. Classes