Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...

Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

This is an advanced undergraduate course on algorithms. This course examines such topics as greedy algorithms, dynamic programming, graph algorithms, string processing, and algorithms for ...

Advanced study in models of computation, programming languages and algorithms with a specific focus on concurrent programming. The course includes models of computation, programming language paradigms ...

This is a preview. Log in through your library . Abstract In an earlier paper [20] combinatorial programming procedures were presented for solving a class of integer programming problems in which all ...

Matlab 7 lets users develop algorithms, analyze data, view data files, and manage projects in signal processing, communication,and test and measurement applications, among others. The developer says ...

How to become a machine learning engineer: A cheat sheet Your email has been sent If you are interested in pursuing a career in AI and don't know where to start, here's your go-to guide for the best ...

Dynamic programming (DP) algorithms have become indispensable in computational biology, addressing problems that range from sequence alignment and phylogenetic inference to RNA secondary structure ...

This is a preview. Log in through your library . Abstract This paper proposes a dynamic programming algorithm for decision CPM (DCPM) networks. DCPM is a natural ...