Welcome to AI and ML for Diagnostics Module! This webpage is the primary source of information for all updates, announcements and content for the parts covered by Fayyaz Minhas. For details on other components, please contact the module organizers.

Announcements

Welcome!

Module Teaching Team

Instructor: Fayyaz MinhasLink opens in a new window

Module Organizers: Deepak Parashar

Teaching Assistants

Name	Email
Ting Zhu	ting.zhu@warwick.ac.uk
Busola Oronti	iyabosola.b.oronti@warwick.ac.uk

Instructor Office HoursLink opens in a new window

Timetable

• All Times are UK Times

Lectures

Time	Location
Wed Nov 13 10:00 - 12:00	A0.39
Wed Nov 20 11:00 - 12:00	A0.39
Wed Nov 27 11:00 - 12:00	A0.39

Lab Sessions

Labs will take place after each lecture. The TAs will be coordinating the lab sessions.

Books and Other resources

[PML] Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy. MIT Press, 2021. link: http://mlbayes.ai/ 

[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)

[Casual Reading:

The master algorithm

The Alignment Problem

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.

NOTE:

• It is strongly recommended that you attend all lectures in person. However, if you are unable to attend lectures due to a genuine issue, you can use the links to archived lecture recordings from my CS909 Machine Learning and Data Mining ModuleLink opens in a new window 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 ).

Dates	Lectures	Reading/Resources	Labs
Lec-1	Classification Methods FrameworkLink opens in a new window Classification and Linear DiscriminantsLink 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 Ensemble Methods and XGBoost	Lecture Slides: PhilosphyLink opens in a new window Linear Discriminants (notes)Link opens in a new window Building Linear Models (notes)Link opens in a new window SVM NotesLink opens in a new window Regularized PerceptronLink opens in a new window Transformations codeLink opens in a new window SVM TutorialLink opens in a new window Trees and XGBoost Resources/Reading: k-Nearest Neighbor AlgorithmLink opens in a new window A few useful things to know about machine learningLink opens in a new window SVM AppletLink opens in a new window Fold and Cut TheoreomLink opens in a new window Book Reading [SVM in PML, SVM in IML] Why Intuition Fails in Higher DimensionsLink opens in a new window and The surprising behavior of distance metrics in high dimensionsal spacesLink opens in a new window and code Link opens in a new window(Optional) Ensemble Methods and XGBoost	Gradient Descent and PerceptronLink opens in a new window [Optional] SVMLink opens in a new window Trees and XGBoostLink opens in a new window Trees and XGBoostLink opens in a new window (make sure to use python3.9Link opens in a new window for this)
Lec-2	Performance Evaluation 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	Chapter 19 “Design and Analysis of Machine Learning Experiments” Alpaydin, Ethem. 2010. Introduction to Machine Learning. Cambridge, Mass.: MIT Press. 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.	Performance Assessment ExerciseLink opens in a new window
Lec-3	Advanced Neural Networks AutoencodersLink opens in a new window Generative ModelsLink opens in a new windowLink opens in a new window GANsLink opens in a new windowLink opens in a new window Barebones GAN in PyTorchLink opens in a new windowLink opens in a new window Diffusion Models Using GANs for generating histology images (By S. Deshpande) [optional]Link opens in a new windowLink opens in a new window Using Graph Neural Netwoks for histology images Link opens in a new windowLink opens in a new window[optional]Link opens in a new window	Generaltive models lectures	Barebones GAN in PyTorchLink opens in a new windowLink opens in a new window Diffusion Model Tutorial [Optional]