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Neuroscience Data Analysis
Explores neuroscience data analysis, emphasizing structured data, computational tools, and the trend of computational neuroscience as a service.
Understanding Phase Transitions in Computational Problems
Explores the connection between phase transitions in physics and computational problems, showcasing how insights from physics can inform algorithm design.
Graph Isomorphisms
Covers the concept of graph isomorphisms, explaining the definition, notation, examples, computational complexity, and number of isomorphism classes.
Diffie-Hellman Key Exchange
Explores the Diffie-Hellman key exchange protocol, covering computational challenges, subgroup issues, and key wrapping in RFC 2631.
Introduction: Course syllabus
Introduces the course syllabus, grading scheme, and classical and quantum computation models.
Programming in C: Basics and Practice
Covers the basics of programming in C, hands-on exercises, and graded assignments.
Hidden Sub-group Problem and Simon Algorithm
Explores the hidden sub-group problem and Simon algorithm in quantum computing.
Graphical Models: Representing Probabilistic Distributions
Covers graphical models for probabilistic distributions using graphs, nodes, and edges.
Theory of Computation: Countability and Undecidable Problems
Explores countability and undecidable problems in the theory of computation.
Recursive Enumerability: Turing Machines and Undecidable Languages
Covers recursively enumerable languages, Turing machines, and the construction of undecidable languages.
