Advanced Algorithmics and Graph Theory in Python
Develop expertise in advanced algorithmic concepts and graph theory principles by solving interactive coding exercises using Python programming language.
Develop expertise in advanced algorithmic concepts and graph theory principles by solving interactive coding exercises using Python programming language.
Dive deep into advanced algorithmics and graph theory while honing your Python programming skills in this engaging, challenge-based course. Tackle real-world problems like pathfinding and routing, implementing and optimizing algorithms to outperform given solutions. From fundamental graph theory to combinatorial game theory, you'll learn to express computational problems, choose appropriate algorithms, and evaluate solutions for complexity and performance. Ideal for engineering students, data scientists, and developers looking to enhance their problem-solving and coding abilities.
Instructors:
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What you'll learn
Express computational problems using graph theory
Select and implement appropriate algorithms for solving complex problems
Code efficient algorithmic solutions in Python
Analyze and evaluate algorithm complexity and performance
Apply graph traversal and routing techniques to real-world scenarios
Understand and implement solutions for NP-complete problems
Skills you'll gain
This course includes:
Scheduled video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course offers an intensive exploration of advanced algorithmics and graph theory, with a focus on practical implementation in Python. Students will engage with challenging problems, such as navigating mazes and optimizing routes, to learn and apply complex algorithms. The curriculum covers a wide range of topics including graph traversal, shortest path algorithms, NP-completeness, heuristics, and combinatorial game theory. Each week introduces new concepts and techniques to improve students' algorithmic thinking and Python programming skills. The course emphasizes hands-on learning, requiring students to develop and refine their own algorithms to outperform given solutions, providing immediate practice of theoretical concepts.
Fundamentals of Graph Theory and Programming Practices
Module 1
Graph Traversal and Data Structures
Module 2
Shortest Paths and Algorithm Complexity
Module 3
NP-Completeness and Advanced Problem Solving
Module 4
Heuristics and Algorithm Trade-offs
Module 5
Combinatorial Game Theory
Module 6
Fee Structure
Instructors
Neural Networks and Signal Processing Expert at IMT Atlantique
Vincent Gripon serves as a permanent researcher at IMT Atlantique, where he specializes in artificial neural networks, deep learning, and signal processing. After earning his MSc from ENS-Cachan and PhD from Télécom Bretagne in 2011, he has established himself as a leading expert in efficient deep learning and graph signal processing. His research contributions include pioneering work in sparse neural networks, few-shot learning, and graph-based approaches to neural network analysis. His impact is evidenced through over 2,900 citations and an h-index of 19, with significant publications in IEEE Transactions and other prestigious journals. His current work focuses on efficient deep learning architectures, associative memories, and FPGA implementations, while maintaining active collaboration with international research teams. His expertise spans theoretical machine learning, graph signal processing, and practical applications in neuroimaging and network optimization. He currently supervises multiple doctoral and postdoctoral researchers while contributing to advancing the field through innovative approaches to neural network design and implementation.
Multi-Criteria Decision Analysis and Operations Research Expert at IMT Atlantique
Patrick Meyer serves as Professor at IMT Atlantique and researcher at the Lab-STICC laboratory (UMR CNRS 6285), where he specializes in multi-criteria decision analysis and operations research. After earning his PhD jointly from the University of Luxembourg and the Engineering Faculty of Mons in 2007, he has established himself as a leading expert in decision support techniques and preference modeling. His research contributions include developing algorithms for preference elicitation and decision support software tools, with over 2,700 citations to his work. His impact spans theoretical developments and practical applications, particularly in the areas of multicriteria decision aiding, operational research, and preference learning algorithms. His multi-disciplinary approach enables him to address real-world challenges posed by industry and public authorities, while maintaining active involvement in software development for decision support systems, including the MCDA package for R and various algorithmic workflows for decision analysis.
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