COMPSCI 274A. Probablistic Learning: Theory and Algorithms. 4 Units.

This question has been a main drive ofSchmidhuber's research since his 1987In 1993 he introduced , and in1994 trained by the"success-story algorithm" His first bias-optimal metalearner was the above-mentioned (2002),and the ultimate metalearner is the (2003).

Numerical Algorithms and their Implementation.9 units (3-3-3); third term.

Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization. In particular, focus will be on first-order methods for both smooth and non-smooth convex function minimization as well as methods for structured convex function minimization, discussing algorithms such as gradient descent, accelerated gradient descent, mirror descent, Newton's method, interior point methods, and more. Prerequisite: multivariable calculus and linear algebra.
Same as: MS&E 213

Master Thesis Genetic Algorithm

COMPSCI 260. Fundamentals of the Design and Analysis of Algorithms. 4 Units.

This course is motivated by problems for which the traditional worst-case analysis of algorithms fails to differentiate meaningfully between different solutions, or recommends an intuitively "wrong" solution over the "right" one. This course studies systematically alternatives to traditional worst-case analysis that nevertheless enable rigorous and robust guarantees on the performance of an algorithm. Topics include: instance optimality; smoothed analysis; parameterized analysis and condition numbers; models of data (pseudorandomness, locality, diffuse adversaries, etc.); average-case analysis; robust distributional analysis; resource augmentation; planted and semi-random graph models. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. Prerequisites: CS161 (required). CS261 is recommended but not required.

Quantum Algorithms and Genetic Code - Thesis - ,,sU LeKeSi

The Department of Computer Science (CS) operates and supports computing facilities for departmental education, research, and administration needs. All CS students have access to the departmental student machine for general use (mail, news, etc.), as well as computer labs with public workstations located in the Gates Building. In addition, most students have access to systems located in their research areas.

Thesis on channel equalization using genetic algorithm

The School’s three departments — Computer Science, Informatics, and Statistics — fuel a wide range of instructional and research efforts, including: design of algorithms and data structures; computer architecture and embedded computer systems; networked and distributed systems; systems software; social and mobile computing; artificial intelligence, machine learning and data mining; computer games and virtual worlds; databases and information retrieval; computer graphics and visualization; bioinformatics, computational biology and genomics; computer-supported cooperative work, human-centered computing and human-computer interaction; security and privacy; software engineering; managerial and social aspects of computing technology; and statistics. The vibrant ICS community continues to explore innovative topics ranging from building complete computer systems on chips smaller than a human fingernail to developing user-interface systems that allow workers on opposite sides of the world to collaborate effectively. ICS research continues to focus on how computing and information technology can be used to solve a broad set of real-world problems, such as improving how first responders communicate during a crisis, optimizing transportation systems, analyzing data to expedite biological research, and improving network security.

Genetic Programming IV: Routine Human-Competitive …

ICS faculty actively lead and participate in a variety of research laboratories, institutes and centers, including the Institute for Genomics and Bioinformatics; Institute for Software Research; Institute for Virtual Environments and Computer Games; California Institute for Telecommunications and Information Technology (Calit2); Data Science Initiative; Center for Machine Learning and Intelligent Systems; Center for Digital Transformation; Center for Emergency Response Technologies; Center for Algorithms and Theory of Computation; Center for Research in Sustainability, Collapse-Preparedness & Information Technology; Secure Computing and Networking Center; Center for Ethnography; Social & Technological Action Research Group; Secure Systems and Software Laboratory; Software Engineering and Analysis Lab; Computational Vision Lab; Transformative Play Lab; and Laboratory for Ubiquitous Computing and Interaction.