CS909 Machine Learning Algorithms and Practice

Welcome to CS909/CS429 MLAP in Term-2 2026! 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.

• Assignment-2 Announced 20 Feb 2026
• 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) and NOT to module organisers/TAs.

Module Teaching Team

Instructor: Fayyaz MinhasLink opens in a new window

MLAP R2D2 — AI Learning Tutor

MLAP R2D2 (reflection, reasoning, dialogue, and design)Link opens in a new window is the AI learning tutor for this module. Lecture materials and supporting resources will be uploaded directly into R2D2, and students can interact with it to help clarify concepts, reflect on your understanding, and think through appropriate problem-solving approaches.

IMPORTANT NOTE:

MLAP R2D2 is provided as an optional learning support tool and is being run on a trial basis. It is not a source of authoritative guidance and may occasionally produce incomplete, inaccurate, or misleading responses. Use of R2D2 is entirely optional and undertaken at the student’s own discretion and responsibility. Neither the University of Warwick nor the module organisers accept responsibility for decisions, actions, or outcomes arising from its use. For official and definitive information on assessments, deadlines, regulations, and requirements, students must always refer to the module webpage, and official announcements. If you encounter issues, unexpected behaviour, or wish to provide feedback on R2D2, please report this via Moodle to the instructors.

Teaching Assistants

Name	Email
George Wright	george.wright.1@warwick.ac.uk
Piotr Keller	piotr.keller@warwick.ac.uk
Kaiwen Zuo	kaiwen.zuo@warwick.ac.uk
Boyang Fu	Boyang.Fu@warwick.ac.uk
Xiaoqing Fan	Xiaoqing.Fan@warwick.ac.uk
Yijie Zhu	Yijie.Zhu@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 George Wright.

Instructor Office HoursLink opens in a new window

(Instructor time table)Link 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 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/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

Time	Location
Monday 11:00 - 12:00 (Weeks 1–10) starting 12 Jan 2026	R0.21
Wednesday 12:00 - 13:00 (Weeks 1–10)	L3
Friday 13:00 - 14:00 (Weeks 1–10)	OC1.05
Revision Lecture 28 April 2026 10am	R0.21

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.

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	CS0.06	George & Boyang	N/A
Lab 2	Thursday 10:00 - 11:00	CS0.01	Piotr & Yijie	N/A
Lab 3	Thursday 11:00 - 12:00	CS0.06	George & Xiaoqing	N/A
Lab 4	Friday 10:00 - 11:00	MB3.17	Yijie & Xiaoqing	N/A
Lab 5	Friday 10:00 - 11:00	CS0.01	Boyang & Piotr	N/A

Coursework Assignments

• Assignment-1 (25% of final mark)
  Assignment Announcement: w/c 19/01/2026 (week 2) via link: https://github.com/foxtrotmike/CS909/tree/master/2026/A1 
  Assignment due: 12 noon Wed 18ᵗʰ February 2026
  Feedback due: Wed 18ᵗʰ March 2026
  Tabula Submission Links: https://tabula.warwick.ac.uk/coursework/submission/efa76db6-755f-4f9f-9b14-462948f4028eLink opens in a new window
• Assignment-2 (MEng: 25% of final mark, MSc: 35% of final mark)
  Assignment Announcement: Week 6 Annouced via this link;Link opens in a new window
  Assignment due: 12 noon Mon 23ʳᵈ March 2026
  Feedback due: Wed 22ⁿᵈ April 2026
  Tabula Submission Links: https://tabula.warwick.ac.uk/coursework/submission/674c08f0-bbac-4b1b-8429-a66274867c1e 
• There will be a final exam in the module. Please check exam deadlines directly with Warwick Student Administrative ServicesLink opens in a new window
• All deadlines: https://warwick.ac.uk/fac/sci/dcs/teaching/deadlines/ 

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/Practicals
1 Jan 12	IntroductionLink opens in a new window Why Data Science? [Self-Study]Link opens in a new window Applications [Self-Study] Link opens in a new window Research Applications [Self-Study]Link opens in a new window FrameworkLink opens in a new window SurveyLink 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 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	Introduction and Philosophy SlidesLink opens in a new window Applications and Framework Slides [Self-Study]Link opens in a new window CRISPR Talk (optional, Self-Study)Link opens in a new window k-Nearest Neighbor AlgorithmLink opens in a new window [Required] [PML] Chapter-1, [IML] Chapter-1 Whole Slide Images are Graphs Talk (Self-Study)Link opens in a new window A few useful things to know about machine learning (recommended self-study)Link opens in a new window Preliminaries (notes)Link opens in a new window Linear Discriminants (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 Perceptron NotesLink opens in a new window SVM NotesLink opens in a new window Fold and Cut TheoreomLink opens in a new window Book Reading [SVM in PML, SVM in IML] SVM TutorialLink opens in a new window	Learning Python Understanding lines Distance and Dot productLink opens in a new window k-Nearest Neighbor DemoLink opens in a new window Implementing kNN classifierLink opens in a new window Understanding lines 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
2 Jan 19	SVM lectures (above) Tree based classifiers (By Ayse Sunar) on Mon 19 Jan	Trees, Random Forest and XGBoost Classifiers (By Dr Ayse Sunar) 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)	From week 1: Preliminaries, Linear Classifiers, SVMs kNN in High dimensionsLink opens in a new window Trees and XGBoostLink opens in a new window Performance Assessment ExerciseLink opens in a new window
3 Jan 26	SVM lectures (above) 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	SVM TutorialLink opens in a new window HistoKernel Paper (optional read) HistoKernel Demo Quantum Kernel SVM for cancer classificationLink opens in a new window (optional read)	Performance Assessment ExerciseLink opens in a new window Work on Assignment-1
4 Feb 02	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	Performance assessment Lecture NotesLink opens in a new window Performance Assessment DemoLink 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 Chapter 19 “Design and Analysis of Machine Learning Experiments” Alpaydin, Ethem. 2010. Introduction to Machine Learning. Cambridge, Mass.: MIT Press. 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.	Performance Assessment ExerciseLink opens in a new window Performance Assessment DemoLink opens in a new window Work on Assignment-1
5 Feb 09	PCA Prelim: Old MacDonald meets LagrangeLink opens in a new window PCA Prelim: Meet stubborn vectorsLink opens in a new window PCA Prelim: Covariance and its friend CorrelationLink opens in a new window PCALink opens in a new window SRM view of PCALink opens in a new window	PCA: Lecture NotesLink opens in a new window (Review lab notebooks as well)	Lagrange Multipliers DemoLink opens in a new window Eigen Vectors DemoLink opens in a new window Eigen Values and Vectors LabLink opens in a new window Information theoretic measuresLink opens in a new window Understanding AssociationLink opens in a new window PCA DemoLink opens in a new window PCA Reconstruction DemoLink opens in a new window PCA TutorialLink opens in a new window Eigen FacesLink opens in a new window Optional: Local PCA for Noise reductionLink opens in a new window Permutation testing for finding noisy components Incremental PCA Robust PCALink opens in a new window Kernelized PCALink opens in a new window ICA TutorialLink opens in a new window MDS TutorialLink opens in a new window Manifold learning methods UMAP ExplorerLink opens in a new window PCA vs LDALink opens in a new window Regression in sklearnLink opens in a new window A deeper look into regression (you can skip the "Advanced" topics)Link opens in a new window Using regression for causal discovery (advanced) Work on Assignment-1
6 Feb 16	Other dimensionality reduction methodsLink opens in a new window: MDS, t-SNE, ICA, LDA, Factor Analysis, Topic Modeling with NMF, CCA (no-video recording) Lecture 3: Interim Module EvaluationLink opens in a new window OLSRLink opens in a new window OLSR to SVRLink opens in a new window Application: Hurricane Intensity RegressionLink opens in a new window How to go beyond classification, regression and dimensionality reductionLink opens in a new window Applied SRM in Barebones PytorchLink opens in a new window One class classifiers (Anomaly Detection)Link opens in a new window RankingLink opens in a new window Recommender SystemsLink opens in a new window Still more problemsLink opens in a new window ClusteringLink opens in a new window Clustering in sklearnLink opens in a new window Ensemble Methods and XGBoost	Dimensionality Reduction Lecture NotesLink opens in a new window Lecture Notes (Regression) Lecture Notes (SRM)Link opens in a new window Finding Anti-CRISPR proteins with ranking (optional)Link opens in a new window Using reinforcement learning to help a mouse escape a cat (optional)Link opens in a new window Detailed Discusison on Causality and Domain Generalization (Optional)Link opens in a new window (Review lab notebooks as well)	PCA Reconstruction DemoLink opens in a new window Incremental PCA Robust PCALink opens in a new window Kernelized PCALink opens in a new window ICA TutorialLink opens in a new window MDS TutorialLink opens in a new window Manifold learning methods UMAP ExplorerLink opens in a new window PCA vs LDALink opens in a new window Regression in sklearnLink opens in a new window A deeper look into regression (you can skip the "Advanced" topics)Link opens in a new window Using regression for causal discovery (advanced) Regression: Regression in sklearnLink opens in a new window A deeper look into regression (you can skip the "Advanced" topics)Link opens in a new window Using regression for causal discovery (advanced)
7 Feb 23	Other machines (from Week 6 continued) NOTE: 2025 lectures are different from previous recordings below and students are recommended to use Lecture CaptureLink opens in a new window accessible via Moodle. Previous recording are available for additional support esp. for re-sit students. Let me pick your brainLink opens in a new window Single Neuron ModelLink opens in a new window Multilayer PerceptronLink opens in a new window Let's play with a neural networkLink opens in a new window Deriving Backpropagation algorithm for MLPsLink opens in a new window  MLP in KerasLink opens in a new window MLP in PyTorch using NN moduleLink opens in a new window MLP in PyTorch for MNIST with DataloadersLink opens in a new window Under the hood view of deep learning librariesLink opens in a new window Improving learning of MLPsLink opens in a new window	Lecture Notes (SRM)Link opens in a new window Deep Learning NotesLink opens in a new window	Understanding REO and SRM: Applied SRM in Barebones PytorchLink opens in a new window Barebones Linear ModelsLink opens in a new window Other ML problems: ClusteringLink opens in a new window Trees and XGBoostLink opens in a new window Multilayer Perceptron: Keras BarebonesLink opens in a new window NN module in PytorchLink opens in a new window MNIST MLP in PyTorchLink opens in a new window Solve the XOR using a single hidden layer BPNN with sigmoid activations Work on Assignment-2 Universal Approximation CodeLink opens in a new window

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