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Introduction to Machine LearningDilum BandaraIntroduction to Machine Learning
Association Analysis
Supervised (inductive) learning
Training data includes desired outputs
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Training data does not include desired outputs
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Training data includes a few desired outputs
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Time Series Analysis and Forecasting in PracticeDilum BandaraThis document discusses time series analysis and forecasting. It covers the components of time series including trends, seasonality, cyclical patterns and irregular components. It then describes several approaches to forecasting including qualitative judgmental methods, statistical time series models and explanatory causal models. Specific statistical time series forecasting techniques are explained such as simple and exponential smoothing, linear regression models, and Holt-Winters seasonal models. The importance of evaluating forecast accuracy is also highlighted.
Introduction to Dimension Reduction with PCADilum BandaraDimension reduction techniques simplify complex datasets by identifying underlying patterns or structures in the data. Principal component analysis (PCA) is a common dimension reduction method that defines new axes (principal components) to maximize variance in the data. PCA examines correlations between these principal components and the original variables to identify sets of highly correlated variables and reduce them to a few representative components. Eigenvalues measure the amount of variance explained by each principal component, and scree plots can help determine how many components to retain by balancing information loss and simplification of the data.
Introduction to Descriptive & Predictive AnalyticsDilum BandaraThis document provides an introduction to descriptive and predictive analytics. It discusses key concepts including descriptive analytics which uses data aggregation and mining to provide insights into past data, predictive analytics which uses statistical models and forecasts to understand the future, and prescriptive analytics which uses optimization and simulation to advise on possible outcomes. The document also reviews basic statistical concepts such as measures of location, dispersion, shape, and association that are important for data analytics. These concepts include mean, median, standard deviation, skewness, kurtosis, and correlation.
Hard to Paralelize Problems: Matrix-Vector and Matrix-MatrixDilum BandaraThe document discusses several problems that are hard to parallelize, including matrix-vector multiplication and matrix-matrix multiplication. It describes 1D and 2D assignment approaches to parallelizing matrix-vector multiplication across multiple processors. 1D assignment distributes the rows of the matrix and vector across processors, while 2D assignment distributes them in a 2D grid. It also outlines map-reduce approaches to parallelizing vector-matrix and matrix-matrix multiplication, breaking the problems into mapping and reducing stages.
Introduction to Map-Reduce Programming with HadoopDilum BandaraThis document provides an overview of MapReduce programming with Hadoop, including descriptions of HDFS architecture, examples of common MapReduce algorithms (word count, mean, sorting, inverted index, distributed grep), and how to write MapReduce clients and customize parts of the MapReduce job like input/output formats, partitioners, and distributed caching of files.
Embarrassingly/Delightfully Parallel ProblemsDilum BandaraThis document discusses embarrassingly parallel problems and the MapReduce programming model. It provides examples of MapReduce functions and how they work. Key points include:
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Introduction to Warehouse-Scale ComputersDilum BandaraWarehouse-Scale Computers and their programming model and workloads. Architectures and cloud computing
Data-Level Parallelism in MicroprocessorsDilum Bandara1. The document discusses data-level parallelism and summarizes vector architectures, SIMD instruction sets, and graphics processing units (GPUs). 2. It describes vector architectures like VMIPS that can perform operations on sets of data elements via vector registers. 3. It also explains how SIMD extensions like SSE exploit fine-grained data parallelism and how GPUs are optimized for data-parallel applications through a multithreaded SIMD execution model.
CPU Pipelining and Hazards - An IntroductionDilum BandaraPipelining is a technique used in computer architecture to overlap the execution of instructions to increase throughput. It works by breaking down instruction execution into a series of steps and allowing subsequent instructions to begin execution before previous ones complete. This allows multiple instructions to be in various stages of completion simultaneously. Pipelining improves performance but introduces hazards such as structural, data, and control hazards that can reduce the ideal speedup if not addressed properly. Control hazards due to branches are particularly challenging to handle efficiently.
Advanced Computer Architecture – An IntroductionDilum BandaraIntroduction to advanced computer architecture, including classes of computers,
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Cost
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Introduction to Content Delivery NetworksDilum BandaraIntroduction to Content Delivery Networks.
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Peer-to-Peer Networking Systems and StreamingDilum BandaraIntroduction to P2P networking, including P2P systems, Unstructured vs. structured overlays, and P2P streaming, distributed hash table
Internet of ThingsDilum BandaraA high-level introduction to the Internet of Things (IoT). Architectures and Inter-Vehicle Communication are also covered.
Wireless Metropolitan Area NetworksDilum BandaraIntroduction to Wireless Metropolitan Area Networks (WMAN) topics like 2G, WAP, 3G
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Wireless sensor networksDilum BandaraIntroduction to wireless sensor networks (WSNs). Cover topics like WSN platforms, transducers/sensors, standards, protocols, powering nodes, and other issues like privacy concerns.
Distributed Memory Programming with MPIDilum BandaraIntroduction to distributed memory programming with MPI. C++ examples on MPI functions, Single-Program Multiple-Data. and collective communication.
Mutual Exclusion in Distributed Memory SystemsDilum BandaraDiscuss mutual exclusion algorithms such as centralized,
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DeadlocksDilum BandaraDiscuss 4 conditions for deadlocks and 4 deadlock prevention techniques. Solutions like Ostrich Algorithm, and detection techniques with 1 resource type. Deadlock recovery and avoidance.
DevNexus - Building 10x Development Organizations.pdfJustin ReockDeveloper Experience is Dead! Long Live Developer Experience!
In this keynote-style session, we’ll take a detailed, granular look at the barriers to productivity developers face today and modern approaches for removing them. 10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method, we invent to deliver products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches works? DORA? SPACE? DevEx? What should we invest in and create urgency behind today so we don’t have the same discussion again in a decade?
Introduction to Warehouse-Scale ComputersDilum BandaraWarehouse-Scale Computers and their programming model and workloads. Architectures and cloud computing
Data-Level Parallelism in MicroprocessorsDilum Bandara1. The document discusses data-level parallelism and summarizes vector architectures, SIMD instruction sets, and graphics processing units (GPUs). 2. It describes vector architectures like VMIPS that can perform operations on sets of data elements via vector registers. 3. It also explains how SIMD extensions like SSE exploit fine-grained data parallelism and how GPUs are optimized for data-parallel applications through a multithreaded SIMD execution model.
CPU Pipelining and Hazards - An IntroductionDilum BandaraPipelining is a technique used in computer architecture to overlap the execution of instructions to increase throughput. It works by breaking down instruction execution into a series of steps and allowing subsequent instructions to begin execution before previous ones complete. This allows multiple instructions to be in various stages of completion simultaneously. Pipelining improves performance but introduces hazards such as structural, data, and control hazards that can reduce the ideal speedup if not addressed properly. Control hazards due to branches are particularly challenging to handle efficiently.
Advanced Computer Architecture – An IntroductionDilum BandaraIntroduction to advanced computer architecture, including classes of computers,
Instruction set architecture, Trends, Technology, Power and energy
Cost
Principles of computer design
Introduction to Content Delivery NetworksDilum BandaraIntroduction to Content Delivery Networks.
CDN design options such as HTTP Redirects, DNS Based, Application Based, Name Based
Peer-to-Peer Networking Systems and StreamingDilum BandaraIntroduction to P2P networking, including P2P systems, Unstructured vs. structured overlays, and P2P streaming, distributed hash table
Internet of ThingsDilum BandaraA high-level introduction to the Internet of Things (IoT). Architectures and Inter-Vehicle Communication are also covered.
Wireless Metropolitan Area NetworksDilum BandaraIntroduction to Wireless Metropolitan Area Networks (WMAN) topics like 2G, WAP, 3G
GPRS, 4G, WiMAX, LTE, and security concerns.
Wireless sensor networksDilum BandaraIntroduction to wireless sensor networks (WSNs). Cover topics like WSN platforms, transducers/sensors, standards, protocols, powering nodes, and other issues like privacy concerns.
Distributed Memory Programming with MPIDilum BandaraIntroduction to distributed memory programming with MPI. C++ examples on MPI functions, Single-Program Multiple-Data. and collective communication.
Mutual Exclusion in Distributed Memory SystemsDilum BandaraDiscuss mutual exclusion algorithms such as centralized,
decentralized, token-based, and distributed algorithms.
DeadlocksDilum BandaraDiscuss 4 conditions for deadlocks and 4 deadlock prevention techniques. Solutions like Ostrich Algorithm, and detection techniques with 1 resource type. Deadlock recovery and avoidance.
DevNexus - Building 10x Development Organizations.pdfJustin ReockDeveloper Experience is Dead! Long Live Developer Experience!
In this keynote-style session, we’ll take a detailed, granular look at the barriers to productivity developers face today and modern approaches for removing them. 10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method, we invent to deliver products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches works? DORA? SPACE? DevEx? What should we invest in and create urgency behind today so we don’t have the same discussion again in a decade?
DealBook of Ukraine: 2025 edition | AVentures CapitalYevgen SysoyevThe DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2024 and the first deals of 2025.
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How Discord Indexes Trillions of Messages: Scaling Search Infrastructure by V...ScyllaDBThis talk shares how Discord scaled their message search infrastructure using Rust, Kubernetes, and a multi-cluster Elasticsearch architecture to achieve better performance, operability, and reliability, while also enabling new search features for Discord users.
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UiPath Agentic Automation Capabilities and OpportunitiesDianaGray10Learn what UiPath Agentic Automation capabilities are and how you can empower your agents with dynamic decision making. In this session we will cover these topics:
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Components of Agents
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❓ If you have any questions or feedback, please refer to the "Women in Automation 2025" dedicated Forum thread. You can find there extra details and updates.
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Formal Methods: Whence and Whither? [Martin Fränzle Festkolloquium, 2025]Jonathan BowenAlan Turing arguably wrote the first paper on formal methods 75 years ago. Since then, there have been claims and counterclaims about formal methods. Tool development has been slow but aided by Moore’s Law with the increasing power of computers. Although formal methods are not widespread in practical usage at a heavyweight level, their influence as crept into software engineering practice to the extent that they are no longer necessarily called formal methods in their use. In addition, in areas where safety and security are important, with the increasing use of computers in such applications, formal methods are a viable way to improve the reliability of such software-based systems. Their use in hardware where a mistake can be very costly is also important. This talk explores the journey of formal methods to the present day and speculates on future directions.
Build with AI on Google Cloud Session #4Margaret Maynard-ReidThis is session #4 of the 5-session online study series with Google Cloud, where we take you onto the journey learning generative AI. You’ll explore the dynamic landscape of Generative AI, gaining both theoretical insights and practical know-how of Google Cloud GenAI tools such as Gemini, Vertex AI, AI agents and Imagen 3.
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