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Programme OverviewPROGRAMME FEE: US$5,000STARTS ON: 31 March 2025DURATION: 20 weeks (4 - 6 Hours/ week)This new year, invest in a learning journey to upskill and gain a competitive edge. Emeritus is collaborating with NUS School of Computing to help you unlock transformative career growth. Enrol before 24 January 2025 using this code: APAC200ALL8364 and get USD200 programme fee benefit. Limited seats to success available. Claim yours now.Application fees: USD60Read more at https://cmu.to/A8Tt0With organisations and consumers being driven by technological innovations, such as Artificial Intelligence, Automation and Data Analytics, organisations are looking for professionals with a deep understanding of these innovations who can foster significant organisational impact. This is the time to boost your career. The NUS School of Computing Technology Leadership and Innovation Programme caters to individuals aspiring to drive strategic transformation in AI, digitalisation and innovation domains. Through the carefully curated modules and capstone project, you develop the requisite mindset and skills to implement impactful strategies using global technological innovations .Programme Modules (Module 1-18)Module 1: Overview of Digital TransformationModule 2: Digital Business ModelsModule 3: Strategy Formulation and Process of Digital TransformationModule 4: Digital Transformation Action PlanModule 5: Role of Data and AnalyticsModule 6: ML-Deep Learning and Neural NetworkModule 7: Robotics and Reinforcement Learning Module 8: Generative AIModule 9: AI and Business ApplicationModule 10: CybersecurityModule 11: Other Technologies: IoT, AR/VRModule 12: Innovation Strategy to Address Digital DisruptionModule 13: System Thinking and Agile MethodologiesModule 14: Implementing Digital InnovationModule 15: Building Team and Digital Culture for TransformationModule 16: Managing Digital Complexities and Change ManagementModule 17: Leading through ESG ChallengeModule 18: Technology and Innovation: Security and Governance

Call for Papers2025 IEEE 2nd International Conference on Deep Learning and Computer VisionJinan|ChinaApr. 18-21, 2025Full paper submission Deadline: Feb. 10th, 2025Acceptance Notification: Mar. 26th, 2025Registration Deadline: Apr. 11th , 2025Conference Date: Apr. 18-21st, 2025Link: https://www.icdlcv.orgContact: icdlcv@126.comYou are kindly encouraged to contribute to and help shape the conference through submissions of your research abstracts and papers to DLCV 2025 conference. Topics of interest for submission include:Track 1: Deep LearningMachine LearningUnsupervised LearningDeep Reinforcement LearningConvolutional Neural Networks (CNN)Deep Hierarchical Networks (DHN)Recurrent Neural Network (RNN)Neuro-Fuzzy AlgorithmsAutoencodersGraph Representation LearningClustering, Classification and RegressionEvolutionary MethodsAutonomic ComputingUser ModelingInformation Retrieval and ReuseQuestion-AnsweringCognitive ArchitecturesDeep Learning Approach in Recommendation SystemsTrack 2: Computer VisionBig data and computer visionBiomedical image analysisImage and video codingImage and video retrievalRemote sensing imageComputational photographyOptimization and method of studySensor and displayData collection and performance analysisComputer vision of deep learningDocument image analysisCharacter identificationAnalysis, behavior identityVisual modelsVideo analysisMultimodal information processingVisual and languageMotion and tracking3D reconstructionHuman-computer interaction

Introduction to Artificial Intelligence with PythonHarvard School of Engineering and Applied SciencesCourse description Link: https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-pythonWhat you'll learnGraph search algorithmsReinforcement learningMachine learningArtificial intelligence principlesHow to design intelligent systemsHow to use AI in Python programs CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You’ll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.

41st Conference on Uncertainty in Artificial IntelligenceRio de Janeiro, BrazilJuly 21-25, 2025Link: https://www.auai.org/uai2025/The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. UAI is supported by the Association for Uncertainty in Artificial Intelligence (AUAI).Important DatesNameDatePaper submission deadline (incl. supp. material)February 10th, 2025 (23:59 Anywhere on Earth, AoE)Author response/discussion periodApril 3rd - April 10th, 2025 (23:59 AoE)Author notificationMay 6th, 2025 (23:59 AoE)Camera-ready deadlineJune 6th, 2025 (23:59 AoE) Subject AreasBelow you find a non-exhaustive list of relevant topics for your reference.AlgorithmsApproximate InferenceBayesian MethodsBelief PropagationExact InferenceKernel MethodsMissing Data HandlingMonte Carlo MethodsOptimization - CombinatorialOptimization - ConvexOptimization - DiscreteOptimization - Non-ConvexProbabilistic ProgrammingRandomized AlgorithmsSpectral MethodsVariational MethodsApplicationsCognitive ScienceComputational BiologyComputer VisionCrowdsourcingEarth System ScienceEducationForensic ScienceHealthcareNatural Language ProcessingNeurosciencePlanning and ControlPrivacy and SecurityRoboticsSocial GoodSustainability and Climate ScienceText and Web DataLearningActive LearningAdversarial LearningCausal LearningClassificationClusteringCompressed Sensing and Dictionary LearningDeep LearningDensity EstimationDimensionality ReductionEnsemble LearningFeature SelectionHashing and EncodingMultitask and Transfer LearningOnline and Anytime LearningPolicy Optimization and Policy LearningRankingReinforcement Learning and BanditsRelational LearningRepresentation LearningSemi-Supervised LearningStructure LearningStructured PredictionUnsupervised LearningModelsFoundation ModelsGenerative ModelsGraphical ModelsModels for Relational DataNeural NetworksProbabilistic CircuitsRegression ModelsSpatial, Temporal and Spatio-Temporal ModelsTopic Models and Latent Variable ModelsPrinciplesCausalityComputational and Statistical Trade-OffsExplainabilityFairnessPrivacyReliabilityRobustness(Structured) SparsityRepresentationConstraintsDempster-Shafer(Description) LogicsImprecise ProbabilitiesInfluence DiagramsKnowledge Representation LanguagesTheoryComputational ComplexityControl TheoryDecision TheoryGame TheoryInformation TheoryLearning TheoryProbability TheoryStatistical Theory