Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

This course is intended to cover information retrieval and other information processing activities, from an applied perspective. There will be numerous programming projects and quizzes. Topics will include: search engine construction (document acquisition, processing, indexing, and querying); learning to rank; information retrieval system performance evaluation; classification and clustering; other machine learning information processing tasks; and many more.

This course is intended to cover information retrieval and other information processing activities, from an applied perspective. There will be numerous programming projects and quizzes. Topics will include: search engine construction (document acquisition, processing, indexing, and querying); learning to rank; information retrieval system performance evaluation; classification and clustering; other machine learning information processing tasks; and many more.

This course is an introduction to computer programming for numerical computing. The course is based on the computer programming language Python and is suitable for students with no programming or numerical computing background who are interested in taking courses in machine learning, natural language processing, or data science. The course will cover fundamental programming, numerical computing, and numerical linear algebra topics, along with the Python libraries that implement the corresponding data structures and algorithms.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering with a focus on real-world applications in a variety of computing contexts (desktop/cluster/cloud). Requires the use of Python.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering with a focus on real-world applications in a variety of computing contexts (desktop/cluster/cloud). Requires the use of Python.

This course introduces foundational abstract data types and algorithms. The main focus is on the use of data structures in designing and developing programs to solve problems in a variety of domains. Specific topics include lists, sets, maps, graphs, stacks, queues, searching, and sorting. (Gen Ed R2)

Prerequisites: COMPSCI 121 (or equivalent experience) and Basic Math Skills (R1). This course is not a substitute for COMPSCI 187. If unsure of whether this course or COMPSCI 187 is more appropriate, contact instructor.

This course introduces foundational abstract data types and algorithms. The main focus is on the use of data structures in designing and developing programs to solve problems in a variety of domains. Specific topics include lists, sets, maps, graphs, stacks, queues, searching, and sorting. (Gen Ed R2)

Prerequisites: COMPSCI 121 (or equivalent experience) and Basic Math Skills (R1). This course is not a substitute for COMPSCI 187. If unsure of whether this course or COMPSCI 187 is more appropriate, contact instructor.

This course introduces foundational abstract data types and algorithms. The main focus is on the use of data structures in designing and developing programs to solve problems in a variety of domains. Specific topics include lists, sets, maps, graphs, stacks, queues, searching, and sorting. (Gen Ed R2)

Prerequisites: COMPSCI 121 (or equivalent experience) and Basic Math Skills (R1). This course is not a substitute for COMPSCI 187. If unsure of whether this course or COMPSCI 187 is more appropriate, contact instructor.