|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
Code generation, register allocation, program optimization, data flow, interprocedural operations, parallel compilation and distributed compilation. Preq: CPSC 8270.
-
3.00 Credits
Search trees; data structures for sets; index structures for data bases; data abstraction and automated implementation; implicit data structures; storage compaction of lists; data structures for decision trees; data structures in areas such as computer graphics, artificial intelligence, picture processing and simulation.
-
3.00 Credits
Preparation for the study of advanced issues in computational complexity, algorithm correctness and inherent limits to computing; set theory and proof techniques; classes of the Chomsky hierarchy. Students are expected to have completed coursework in formal languages and automata before enrolling in this course.
-
3.00 Credits
Basic techniques for design and analysis of algorithms; models and techniques for obtaining upper and lower time and space bounds; time/space trade-offs; inherently difficult problems. Students are expected to have completed coursework in discrete mathematics before enrolling in this course.
-
3.00 Credits
Covers the use of algorithms in machine learning, including decision trees, Bayesian learning, genetic algorithms, and reinforcement learning. This course also covers basic theoretical concepts, such as Occam's razor, inductive bias, VC dimension, and PAC learnability. Students are expected to have familiarity with programming, linear algebra, and statistics before enrolling in this course.
-
3.00 Credits
Covers the principles of recently developed algorithms in deep learning, such as convolutional neural networks, recursive neural networks, generative adversarial nets and deep reinforcement learning, self-attention, deep neural networks, auto encoders, and VAE. Students are expected to have familiarity with programming, linear algebra, and statistics before enrolling in this course.
-
3.00 Credits
Covers algorithms such as dynamic programming for biological problems, including sequence alignment and phylogeny tree constructions; statistical and mathematical modeling of high throughput data, such as differentially expressed genes from microarray data and HMM for gene prediction; graph and network theory for biological networks.
-
3.00 Credits
Basic theory and practical algorithms in information retrieval, including indexing, vector space models, evaluation methods, probabilistic and language models of information retrieval, and web search.
-
3.00 Credits
Networks and network models arise in many places, from physical complex systems, communications, and electrical circuits, to social science and bioinformatics. This course teaches the common theory of abstract and real-world networks, including models, metrics, visualization, representation, comparison and organization. Students are expected to have basic programming skills and introductory knowledge of linear algebra, probability and statistics before enrolling in this course.
-
3.00 Credits
Basic techniques and fundamental problems of numerical computation with an emphasis on big data. Focus is placed on practical data analysis questions that arise in areas such as engineering, health care, natural science and economics. Methods are discussed in the context of machine learning, data mining and computational problems on graphs. Students are expected to have basic knowledge of linear algebra, calculus, programming and data structures before enrolling in this course.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|