Search result for Courses taught by Alexander S. Kulikov

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  • Computer Science(13)
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  • Algorithms(13)
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Description of Algorithms Courses
  • Algorithms is a course that focuses on the design and implementation of efficient algorithms for solving problems. This course is essential for computer science students and professionals who want to develop their skills in problem solving and software design.
Common Things Learned
  • In Algorithms courses, students learn about different algorithms and their complexity, time and space complexity analysis, sorting algorithms, searching algorithms, graph algorithms, dynamic programming, and much more. They also learn how to analyze the efficiency of an algorithm, and how to develop algorithms that optimize performance for specific situations.
Typical Student
  • Typical students of Algorithms courses are computer science majors, software engineers, and other professionals who want to improve their problem-solving and software design skills. These students have a strong foundation in mathematics and programming, and are looking to develop their skills in algorithm design and analysis.
 
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Algorithmic Toolbox

by Alexander S. Kulikov , Michael Levin , Neil Rhodes , Pavel Pevzner , Daniel M Kane
  • 4.6
Approx. 39 hours to complete
The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second). Programming Challenges Welcome!...
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Mathematical Thinking in Computer Science

by Alexander S. Kulikov , Michael Levin , Владимир Подольский
  • 4.4
Approx. 40 hours to complete
Mathematical thinking is crucial in all areas of computer science: algorithms, bioinformatics, computer graphics, data science, machine learning, etc. In this course, we will learn the most important tools used in discrete mathematics: induction, recursion, logic, invariants, examples, optimality. We will use these tools to answer typical programming questions like: How can we be certain a solution exists?...
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Competitive Programmer's Core Skills

by Alexander S. Kulikov , Alexander Logunov , Kirill Simonov , Aliaksei Tolstsikau
  • 4.6
Approx. 32 hours to complete
During the course, you’ll learn everything needed to participate in real competitions — that’s the main goal. Along the way you’ll also gain useful skills for which competitive programmers are so highly valued by employers: ability to write efficient, reliable, and compact code, manage your time well when it’s limited, apply basic algorithmic ideas to real problems, etc....
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Data Structures

by Alexander S. Kulikov , Michael Levin , Daniel M Kane , Neil Rhodes
  • 4.6
Approx. 25 hours to complete
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments....
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Introduction to Graph Theory

by Alexander S. Kulikov
  • 4.5
Approx. 21 hours to complete
We invite you to a fascinating journey into Graph Theory — an area which connects the elegance of painting and the rigor of mathematics; is simple, but not unsophisticated. Graph Theory gives us, both an easy way to pictorially represent many major mathematical results, and insights into the deep theories behind them....
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Computational Geometry

by Alexander S. Kulikov , Aliaksei Tolstsikau , Kira Vyatkina
  • 3.9
Approx. 19 hours to complete
This course represents an introduction to computational geometry – a branch of algorithm theory that aims at solving problems about geometric objects. Its application areas include computer graphics, computer-aided design and geographic information systems, robotics, and many others. Point inclusion in a polygon 1. 1 Introduction 1. 2 Problem statement 1....
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Number Theory and Cryptography

by Alexander S. Kulikov , Michael Levin , Владимир Подольский
  • 4.5
Approx. 19 hours to complete
We all learn numbers from the childhood. Some of us like to count, others hate it, but any person uses numbers everyday to buy things, pay for services, estimated time and necessary resources. People have been wondering about numbers’ properties for thousands of years. Famous 20th century mathematician G. H. You will even pass a cryptographic quest!...
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Combinatorics and Probability

by Alexander S. Kulikov , Владимир Подольский
  • 4.6
Approx. 21 hours to complete
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? All these questions are addressed by a mathematical field called Combinatorics....
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Algorithms on Graphs

by Alexander S. Kulikov , Michael Levin , Daniel M Kane , Neil Rhodes
  • 4.7
Approx. 55 hours to complete
If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. In this course, you will first learn what a graph is and what are some of the most important properties. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project....
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Algorithms on Strings

by Alexander S. Kulikov , Michael Levin , Pavel Pevzner , Neil Rhodes
  • 4.5
Approx. 18 hours to complete
World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Suffix Trees...
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Time
  • To get the fundamentals of Algorithms, it usually takes around one semester of study, which is about 4-5 months. However, to become well adept in this topic, it requires years of practice and experience. Students need to constantly practice their algorithm design and analysis skills by solving problems and implementing algorithms in real-world scenarios.
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Fields

  • Algorithms is used in various fields, such as computer science, engineering, biology, finance, and many others. In computer science, algorithms are used to solve complex problems and optimize performance. In engineering, algorithms are used in the design and analysis of systems. In biology, algorithms are used to analyze genetic data and model biological systems. In finance, algorithms are used to analyze market data and make investment decisions. Algorithms are also used in many other fields that require mathematical modeling and optimization.

  • Related Fields
Careers

  • Algorithms is needed in many specific careers, such as software engineering, data science, artificial intelligence, finance, and many others. In software engineering, algorithms are used to develop efficient and scalable software systems. In data science, algorithms are used to analyze and model large data sets. In artificial intelligence, algorithms are used to develop intelligent systems that can learn and adapt. In finance, algorithms are used to analyze market data and make investment decisions.

  • Examples of Common Careers
    • Software Engineer
    • Data Scientist
    • Artificial Intelligence Engineer
    • Quantitative Analyst
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