Wie entwickle ich Software? Algorithmen auf Basis von Java zum Suchen und Sortieren werden vorgestellt und die dazu benötigten Datenstrukturen eingeführt. Erfolgreiche Absolventen können 6 ECTS-Credits erwerben.
Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。]
This course will cover some of the common algorithms underlying the following fundamental topics in bioinformatics: assembling genomes, comparing DNA and protein sequences, finding regulatory motifs, analyzing genome rearrangements, identifying proteins, and many other topics.
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.
Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations.
Modéliser un problème, concevoir un algorithme de résolution et en proposer une implémentation correcte. Du problème à sa solution, ce cours combine approches pragmatique, pratique et théorique de l'informatique.
This course is for experienced C programmers who want to program in C++. The examples and exercises require a basic understanding of algorithms and object-oriented software.
In this eleven-week course, you'll learn about the tools used by complex systems scientists to understand, and sometimes to control, complex systems.
Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."
In this course you will examine real world problems -- rescue the Apollo 13 astronauts, stop the spread of epidemics, and fight forest fires -- involving differential equations and figure out how to solve them using numerical methods.
This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.
This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems!
In this course you will learn several fundamental principles of advanced algorithm design: greedy algorithms and applications; dynamic programming and applications; NP-completeness and what it means for the algorithm designer; the design and analysis of heuristics; and more.
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers basic iterable data types, sorting, and searching algorithms.
Cryptography is essentially the science of writing in secret code. In data and telecommunications, cryptography has specific security requirements, such as authentication, privacy or confidentiality, integrity, and non-repudiation. To meet these security requirements, we employ secret key (or symmetric) cryptography, public-key (or asymmetric) cryptography, and hash functions.
Solving Hard Problems.
Using CUDA to Harness the Power of GPUs.
The advent of computers transformed science. Large, complicated datasets that once took researchers years to manually analyze could suddenly be analyzed within a week using computer software. Computational biology refers to the use of computers to automate data analysis or model hypotheses in the field of biology.
6.00x is an introduction to using computation to solve real problems. The course is aimed at students with little or no prior programming experience who have a desire (or at least a need) to understand computational approaches to problem solving.