Do you wish to be a data scientist but don't know where to begin? Want to implement statistics for data science? Want to get acquainted with R programs? Want to learn about the logic involved in computing statistics? If so, then this is the course for you.

Many methods for automated software test generation, including some that explicitly use machine learning (and some that use ML more broadly conceived) derive new tests from existing tests (often referred to as seeds). Often, the seed tests from which new tests are derived are manually constructed, or at least simpler than the tests that are produced as the final outputs of such test generators. We propose annotation of generated tests with a provenance (trail) showing how individual generated tests of interest (especially failing tests) derive from seed tests, and how the population of generated tests relates to the original seed tests. In some cases, post-processing of generated tests can invalidate provenance information, in which case we also propose a method for attempting to construct "pseudo-provenance" describing how the tests could have been (partly) generated from seeds.

As simple as it may sound, text mining involves deriving important, high quality information from text. What do we get from this high quality information? Pretty much anything; text categorization, sentiment analysis, document summarization to name a few.

Research in AI within the Computer Science Department at the University of California, Los Angeles is loosely composed of three interacting and cooperating groups: (1) the Artificial Intelligence Laboratory, at 3677 Boelter Hall, which is concerned mainly with natural language processing and cognitive modelling, (2) the Cognitive Systems Laboratory, at 4731 Boelter Hall, which studies the nature of search, logic programming, heuristics, and formal methods, and (3) the Robotics and Vision Laboratory, at 3532 Boelter Hall, where research concentrates on robot control in manufacturing, pattern recognition, and expert systems for real-time processing.

This course gives you a complete insight into the modern design of digital systems fundamentals from an eminently practical point of view. Unlike other more "classic" digital circuits courses, our interest focuses more on the system than on the electronics that support it. This approach will allow us to lay the foundation for the design of complex digital systems. You will learn a set of design methodologies and will use a set of (educational-oriented) computer-aided-design tools (CAD) that will allow you not only to design small and medium size circuits, but also to access to higher level courses covering so exciting topics as application specific integrated circuits (ASICs) design or computer architecture, to give just two examples. Course topics are complemented with the design of a simple processor, introduced as a transversal example of a complex digital system.