EE5M16MachineLearningwithApplicationsinMediaEngineering[5credits]Lecturer(s): UssherAssistantProfessorFrançoisPitié ModuleorganisationSemester Start
WeekEndWeek
AssociatedPracticalHours
Lectures TutorialsPerWeek Total PerWeek Total
1 1 12 20 2 22 1 11TotalcontactHours:53
ModuledescriptionThis module is an introduction to Machine Learning (ML), with a focus on DeepLearning.Deep learning isanothernameforartificialneuralnetworks,whicharealooselyinspiredbythestructureoftheneuronsinthecerebralcortex.AlthoughDeepLearninghasbeenaroundforquiteawhile,ithasrecentlybecomeadisruptive technology that has been unexpectedly taking over operations oftechnologycompaniesaroundtheworldanddisruptingallaspectsofsociety.WhenyoureadorhearaboutAIormachineLearningsuccessesinthenews,itreallymeansDeepLearningsuccesses.The course starts with an introduction to some essential aspects of MachineLearning,includingLeastSquares,LogisticRegressionandaquickoverviewofsomepopularclassificationtechniques.Then the course will dive into the fundamentals of Neural Nets, including FeedForwardNeuralNets,ConvolutionNeuralNetsandRecurrentNeuralNets.The material is constructed in collaboration with leading industrial practitionersincludingGoogle,YouTubeandMovidius,andstudentswillhaveguestlecturesfromthesecompanies.HandsonlabswillgiveyouexperiencewiththefieldandallowyoutodevelopyourownDeepLearningapplications.LearningoutcomesOncompletionofthismodule,thestudentwillbeableto:
1. Describe the main neural network architectures and parameters used inpopularDeepLearningsoftwarelibrariessuchasKeras.
2. Implementneuralnetworkapplicationsusingpython3andkeras
3. Evaluate the performance ofMachine Learning algorithms and analyse thepotentialpitfalls.
TeachingstrategyThe teaching strategy for this module is a mixture of lectures, problem-solvingtutorials and laboratories dedicated to implement and solve machine learningproblems.MostofthetheoreticalelementsofMachineLearningsandDeepLearningwill be covered in the first half of the term. The rest of the term is dedicated toexposuretomoreadvancedlabsandexposuretoindustryrelatedproblems.ProgrammingEnvironmentWehavedevelopedafantasticlabenvironmentspeciallyforthismodule,sothatyoucanlearnbestindustrypractices:
• programming will be done in python 3 using Keras and TensorFlow.Everything will be running on the Google Cloud Platform, which gives on-demandscalablecomputingresources.
• thecodingenvironmentwillbeacombinationofshell/terminal,editor,andJupyternotebook.
• Gitwillbeusedtocheckpointlabprogressandgivecontinuousfeed-backonlabassignments.
Wehavedevelopedaplatformthatwill smoothoutall thepainful installationandconfigurationpartssothatyoucanfocusontheessential.AssessmentThe finalwritten end-of-year examination counts for 75%of overallmark, the labsubmissionsfor20%andamid-termquizinweek8fortheremaining5%.Notethatit is expected that the student shouldbe able to complete theproblemsposed inlaboratoriesduringthetimetabledhours.Textbooks
• DeepLearning,IanGoodfellowetal.,(MITpress),[https://www.deeplearningbook.org]
• MachineLearningonCousera,AndrewNg[https://www.coursera.org/learn/machine-learning]
• NeuralNetworksandDeepLearning,MichaelNielsen[http://neuralnetworksanddeeplearning.com/]
