Vassilios Rendoumis successfully completed Coursera's online offering of Linear and Discrete Optimization from EPFL with distinction on June 02, 2014. The advanced undergraduate course covered basic principles of linear programming including the simplex algorithm, its complexity, and duality, and provided an introduction to discrete optimization through bipartite matchings, shortest paths, and the primal/dual method. The document does not confer credit or a degree from EPFL.
This course provided an introduction on how to think using
models. Specific topics included, among others, decision-making, tipping points, economic models, crowd dynamics, Markov processes, game theory and predictive thinking.
This course is part II of an introduction to the theory and practice of financial engineering and risk management. The course focused on portfolio optimization and the CAPM as well as the mechanics and pricing of derivative securities in various asset classes.
Coding the Matrix: Linear Algebra through Computer Science ApplicationsVassilios Rendoumis
In this class, you have learned key concepts and methods of linear algebra, using them to think about problems in computer science. You have implemented basic matrix and vector functionality and algorithms, and used them to process real-world data.
This course covered a broad set of topics critical to practical data science: relational databases, MapReduce, NoSQL, selected topics
in statistical modeling, selected topics in machine learning, and information visualization, and a variety of algorithmic topics.
This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.
This introductory course taught students the basics of interactive programming in Python. Students built a collection of simple interactive games to solidify their understanding of the material.
This course is an introduction to the theory and practice of
financial engineering and risk management. The course focused on the mechanics and pricing of derivative securities in various asset classes.
This course provided an introduction on how to think using
models. Specific topics included, among others, decision-making, tipping points, economic models, crowd dynamics, Markov processes, game theory and predictive thinking.
This course is part II of an introduction to the theory and practice of financial engineering and risk management. The course focused on portfolio optimization and the CAPM as well as the mechanics and pricing of derivative securities in various asset classes.
Coding the Matrix: Linear Algebra through Computer Science ApplicationsVassilios Rendoumis
In this class, you have learned key concepts and methods of linear algebra, using them to think about problems in computer science. You have implemented basic matrix and vector functionality and algorithms, and used them to process real-world data.
This course covered a broad set of topics critical to practical data science: relational databases, MapReduce, NoSQL, selected topics
in statistical modeling, selected topics in machine learning, and information visualization, and a variety of algorithmic topics.
This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.
This introductory course taught students the basics of interactive programming in Python. Students built a collection of simple interactive games to solidify their understanding of the material.
This course is an introduction to the theory and practice of
financial engineering and risk management. The course focused on the mechanics and pricing of derivative securities in various asset classes.
This advanced undergraduate programming course covers the principles of functional programming using Scala, including the use of functions as values, recursion, immutability, pattern matching, higher-order functions and collections, and lazy
evaluation.
Go Packages, Imports, Function & Return Types, Slices and Slice Range Syntax, Custom Type Declaration, Receiver Functions, Multiple Return Types, Byte Slices, Joins on Slices of Strings, Error Handling, Testing, Random Number Generation, Element Assertion in Slices, Structs (Declaration, Definition), Updating and Embedding of Structs, Structures with Receiver Functions, Pass by Value, Structs with Pointers, Pointer Operations, Pointer Shortcuts, Reference vs Value Types, Maps, Manipulating Maps, Iterating over Maps, Maps vs Structs, Interfaces, Reader and Writer Interface, Go Routines, Channels, Channel Implementation, Blocking Channels, Repeating Routines, Alternative Loop Syntax, Sleeping a Routine, Function Literals
This advanced undergraduate programming course covers the principles of functional programming using Scala, including the use of functions as values, recursion, immutability, pattern matching, higher-order functions and collections, and lazy
evaluation.
Go Packages, Imports, Function & Return Types, Slices and Slice Range Syntax, Custom Type Declaration, Receiver Functions, Multiple Return Types, Byte Slices, Joins on Slices of Strings, Error Handling, Testing, Random Number Generation, Element Assertion in Slices, Structs (Declaration, Definition), Updating and Embedding of Structs, Structures with Receiver Functions, Pass by Value, Structs with Pointers, Pointer Operations, Pointer Shortcuts, Reference vs Value Types, Maps, Manipulating Maps, Iterating over Maps, Maps vs Structs, Interfaces, Reader and Writer Interface, Go Routines, Channels, Channel Implementation, Blocking Channels, Repeating Routines, Alternative Loop Syntax, Sleeping a Routine, Function Literals
Configuring applications on Kubernetes, Running jobs on Kubernetes, Managing application health on Kubernetes,
Monitoring and troubleshooting applications on Kubernetes, Helm deep dive, Kubernetes under the hood, Spinning a
Kubernetes cluster, Managing resources on a Kubernetes cluster, Role-based Access Control (RBAC) on a Kubernetes
cluster, Kubernetes Networking, Microservice Architecture and development with Docker, Kubernetes blackbelt
Write C programmes independently with in-depth understanding of POINTERS, dynamic memory allocation, recursions, Arrays, Strings, functions, file handling, command line arguments, bitwise operators. Complete command over branching using if-else statement and loops (while, for and do-while) with extensive practical examples and assignments. Understand clearly Arrays and Strings, sorting arrays using bubble sort and various standard string functions. Write your own FUNCTIONS and create custom User Defined Library. Learn all about POINTERS, functions to allocate dynamic memory - malloc, calloc and realloc and free functions. Relationship between arrays and pointers. Array of pointers and simulating a dynamic 2D array using array of pointer. Command line parameter passing. File handling in details. Bitwise operators. Recursion - how it works, recursion vs iteration in depth discussion.
This course teaches basic arithmetic and variables, how to handle data structures such as Python lists, Numpy arrays and Pandas DataFrames. It introduces the Python functions and control flow as well as data visualization with Python in order to create visualizations on real data.
This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including feature extraction, supervised learning, model evaluation, and exploratory data analysis. Students will gain hands-on experience applying these principles by using Apache Spark to implement several scalable learning pipelines.
In this course students learned what the expected output of Data Scientist is and how they can use PySpark (part of Apache Spark) to deliver against these expectations. The course assignments included Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.
Social physics is a big data science that models how networks of people behave and uses these network models to create actionable intelligence. It is a quantitative science that can accurately predict patterns of human behavior and guide how to influence those patterns to (for instance) increase decision making accuracy or productivity within an organization. Included in this course is a survey of methods for increasing communication quality within an organization, approaches to providing greater protection for personal privacy, and general strategies for increasing resistance to cyber attack.
This graduate-level course introduces students to a variety of models and techniques for analyzing social and economic networks, including random graph models, statistical models, and game theoretic models of network formation, diffusion, learning, and peer effects.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
This course covers computational aspects of investing, including: Company valuation, the Capital Assets Pricing Model, Efficient Markets Hypothesis, the role of information in pricing, historical data and its manipulation, portfolio performance assessment and optimization.
In this course students learn programming in R, reading data into
R, creating data graphics, accessing and installing R packages,
writing R functions, debugging, and organizing and commenting
R code.
This course provides an introduction to computer programming using Python. Topics include elementary data types (numeric types, strings, lists, tuples, dictionaries and files), control flow (if, for, while), functions, modules, objects, methods, fields and mutability.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
The Art Pastor's Guide to Sabbath | Steve Thomason
Linear and Discrete Optimization
1. coursera.org
Statement of Accomplishment
WITH DISTINCTION
JUNE 02, 2014
VASSILIOS RENDOUMIS
HAS SUCCESSFULLY COMPLETED THE ECOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE’S ONLINE
OFFERING OF
Linear and Discrete Optimization
This advanced undergraduate course treats basic principles on
linear programming like the simplex algorithm, its complexity,
and duality. Furthermore it gives an introduction on discrete
optimization via bipartite matchings, shortest paths and the
primal/dual method.
PROFESSOR FRIEDRICH EISENBRAND
EPFL
DISCLAIMER : THIS ONLINE OFFERING DOES NOT REFLECT THE ENTIRE CURRICULUM OFFERED TO STUDENTS ENROLLED AT ECOLE
POLYTECHNIQUE FÉDÉRALE DE LAUSANNE. THIS DOCUMENT DOES NOT AFFIRM THAT THIS STUDENT WAS ENROLLED AS A ECOLE
POLYTECHNIQUE FÉDÉRALE DE LAUSANNE STUDENT IN ANY WAY; IT DOES NOT CONFER A ECOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
CREDIT; IT DOES NOT CONFER A ECOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE DEGREE OR CERTIFICATE; AND IT DOES NOT VERIFY THE
IDENTITY OF THE INDIVIDUAL WHO TOOK THE COURSE.