The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to implement abstract data collections and their associated operations. Specific implementations include linked structures, recursive structures, binary trees, balanced trees, and hash tables. Algorithm analysis and asymptotic bounding of implementations is a major topic throughout the course. The topics covered in this course are fundamental to programming and are essential to further computer science courses.

The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to implement abstract data collections and their associated operations. Specific implementations include linked structures, recursive structures, binary trees, balanced trees, and hash tables. Algorithm analysis and asymptotic bounding of implementations is a major topic throughout the course. The topics covered in this course are fundamental to programming and are essential to further computer science courses.

Computing has become data-driven, and databases are now at the heart of commercial applications. The purpose of this course is to provide a comprehensive introduction to the use of data management systems within the context of various applications. Some of the covered topics include application-driven database design, schema refinement, implementation of basic transactions, data on the web, and data visualization.

Development of mathematical reasoning skills for problems that involve uncertainty. Counting and probability -- basic counting problems, probability definitions, mean, variance, binomial distribution, discrete random variables, continuous random variables, Markov and Chebyshev bounds, Laws of large number, and central limit theorem. Probabilistic reasoning -- conditional probability and odds, Bayes' Law, Markov Chains, Bayesian Network, Markov Decision Processes.

Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science.

The Course explores key concepts of artificial intelligence, including state-space and heuristic search techniques, game playing, knowledge representation, automated planning, reasoning under uncertainty, decision theory and machine learning. We will examine how these concepts are applied in the context of several applications.

Practical experience in the management of software development projects. Managing people, coaching, mentoring, planning, scheduling, evaluation, negotiation, and expectation management are explored. The focus is on the power of communication.

Development of mathematical reasoning skills for problems that involve uncertainty. Counting and probability -- basic counting problems, probability definitions, mean, variance, binomial distribution, discrete random variables, continuous random variables, Markov and Chebyshev bounds, Laws of large number, and central limit theorem. Probabilistic reasoning -- conditional probability and odds, Bayes' Law, Markov Chains, Bayesian Network, Markov Decision Processes.

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.