Courses tagged with "Evaluation" (161)

This is a graduate course on the design and analysis of algorithms, covering several advanced topics not studied in typical introductory courses on algorithms. It is especially designed for doctoral students interested in theoretical computer science.

Part 2 of the UC Berkeley Agile Development Using Ruby on Rails XSeries Program will teach you to use JavaScript to enhance applications and create more sophisticated apps by adding relationships between models within the Ruby on Rails framework. You will also learn about what happens after the apps are deployed to real users, including how to monitor performance, identify and fix common performance problems, and avoid compromising customer data. Finally, learners will see how to apply Agile techniques to enhance and refactor legacy code and practice app deployment to real users to monitor performance, identify and fix common performance problems, and avoid compromising customer data. 

Other topics covered in this software engineering course include:

• How to form, organize and manage small programming teams
• Introduction to design patterns: what they are and how to recognize opportunities to apply them
• Using Rails for more advanced features like third-party authentication and elegantly expressing design patterns that arise frequently in SaaS

There will be four homework assignments: two programming assignments, an open source assignment and one assignment about operations/deployment. There will also be several short quizzes. The videos and homework assignments used in this offering of the course were revised in October 2016.

This intermediate computer programming course uncovers how to code long-lasting software using highly-productive Agile techniques to develop Software as a Service (SaaS) using Ruby on Rails. You will understand the new challenges and opportunities of SaaS versus shrink-wrapped software and learn to apply fundamental Rails programming techniques to the design, development, testing, and public cloud deployment of an Software as a Service (SaaS) application

Using best-of-breed tools that support modern development techniques including Behavior-Driven design, user stories, Test-Driven Development, velocity, and pair programming, learners will discover how modern programming language features in Ruby on Rails can improve productivity and code maintainability. 

Weekly coding projects and quizzes will be part of the learning experience in this SaaS course. Those who successfully complete the assignments and earn a passing grade can get a verified certificate from BerkeleyX. The videos and homework assignments have been updated to use Ruby 2, Rails 4 and RSpec 3. The new class also includes embedded live chat with Teaching Assistants and other students and remote pair programming with other students.

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

Algorithms power the biggest web companies and the most promising startups. Interviews at tech companies start with questions that probe for good algorithm thinking.

In this computer science course, you will learn how to think about algorithms and create them using sorting techniques such as quick sort and merge sort, and searching algorithms, median finding, and order statistics.

The course progresses with Numerical, String, and Geometric algorithms like Polynomial Multiplication, Matrix Operations, GCD, Pattern Matching, Subsequences, Sweep, and Convex Hull. It concludes with graph algorithms like shortest path and spanning tree.

Topics covered:

• Sorting and Searching
• Numerical Algorithms
• String Algorithms
• Geometric Algorithms
• Graph Algorithms

This course is part of the Fundamentals of Computer Science XSeries Program: 

• Programming Basics
• Object-Oriented Programming
• Foundations of Data Structures
• Implementation of Data Structures

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.

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.

In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures (heaps, hash tables, search trees), randomized algorithms, and more.

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 is designed to be a fun introduction to the basics of programming in Python. Our main focus will be on building simple interactive games such as Pong, Blackjack and Asteroids.

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.

What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?

They are all complex real world problems being solved with applications of intelligence (AI).

This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.

Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.

The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.

This course covers finite automata, context-free grammars, Turing machines, undecidable problems, and intractable problems (NP-completeness).

Start learning how to program video games using the C# programming language. Plenty of practice opportunities are included!

Learn various methods of analysis including: unsupervised clustering, gene-set enrichment analyses, Bayesian integration, network visualization, and supervised machine learning applications to LINCS data and other relevant Big Data from high content molecular and phenotype profiling of human cells.

This course was the first in a two-part series covering some of the algorithms underlying bioinformatics. It has now been split into three smaller courses.

This is the second course in a two-part series on bioinformatics algorithms, covering the following topics: evolutionary tree reconstruction, applications of combinatorial pattern matching for read mapping, gene regulatory analysis, protein classification, computational proteomics, and computational aspects of human genetics.