Computer Science

This course examines algorithms and representational schemes used in artificial intelligence, AI search techniques (e.g., heuristic search, constraint satisfaction, etc.) for solving both optimal and satisficing tasks, tasks such as game playing (adversarial search), planning, and natural language processing. It discusses and examines the history and future of AI and the ethics surrounding the use of AI in society.

This course aids in the acquisition of basic knowledge about algorithms and data structures. It discusses and instructs on evaluation methods and programming techniques for making good programs.
 

Prerequisites for regular course students are "Practice of Information Processing" and "Computer Seminar I." Taking "Fundamentals of Information Science I" is strongly recommended. Prerequisites for JYPE/DEEP/IMAC-U students are similar to the courses above.
Students should have some knowledge of computer languages, preferably C or Python.

This course focuses on prototyping an internet-based communication system. In the course, a solution is implemented that uses a modular system where the students can set their own design goals.  Assumed knowledge in programming is required.

This course starts with a historical overview of computer simulations in science and engineering and an introduction to the challenges and opportunities in connecting simulations, theory, and experiments. Students address the core concepts essential to understand and interpret computer simulations in science and engineering, including the fundamentals of statistical physics, interaction potentials, Monte Carlo simulations, equation-based simulations, and the concept of coarse-grained simulations and enhanced sampling techniques. 

This course is designed to provide a comprehensive introduction to the modern study of computer algorithms. It presents many algorithms and covers them in considerable depth. Each chapter presents an algorithm, a design technique, an application area or a related topic. Since we emphasize efficiency as a design criterion, we include careful analyses of the running time of all our algorithms. In addition to the introduction of “design of algorithms”, we also play the emphasis on the “complexity analysis of algorithms” to help students understand the detailed differences between various algorithms for a certain problem mainly in terms of time. The carefully chosen English material is intended to provide the students an enjoyable taste for the international class on algorithms. The textbook we chose is also used by many other universities for undergraduate algorithm course. The course targets the enhancement of the following skills: 1)understanding and mastering the fundamental algorithm design by a series representative algorithms such as: graph algorithms, sorting algorithms etc.; 2) training the capability of algorithms analysis as well the proof of the correctness of algorithms in terms of time complexity and asymptotic efficiency, improving the logic reasoning and understanding the development of algorithm theory; 3) encouraging students to have a depth understanding of studied algorithm by applying them to practical applications as well as problems, training them to relate what they have learned in the class to the real-world problems.4) improving the capability of solving real-world problems.

This course surveys research methods for science and technology studies and across the social sciences that contribute to the generation of new data. Students study a diverse range of methods and learn to understand the strengths and weaknesses of particular methods for investigating particular questions. Students are introduced to the theory and practice of qualitative and quantitative methods. Topics include research ethics, research design, face-to-face interviews and focus groups, surveys, content and discourse analysis, and ethnography. 

Students learn fundamental theories and methods of database systems: what they are, how they are developed, and how they function to achieve their purposes. The course exemplifies these constructs with contemporary database technologies and students learn how these technologies are exploited to build effective information systems of different scale.

The course helps students to become confident with a range of data structures and algorithms and able to apply them in realistic situations. The course provides the tools required to analyze a problem and decide which algorithms or algorithmic techniques to apply to solve it. The course involves practical programming and encourages a thoughtful approach to analysis and design problems.

The course presents an application-focused and hands-on approach to learning neural networks and reinforcement learning. It is an introduction to deep learning methods, presenting a wide range of connectionist models that represent the current state-of-the-art. Topics include the fundamentals of machine learning and the mathematical and computational prerequisites for deep learning; feed-forward neural networks, convolutional neural networks, and the recurrent connections to a feed-forward neural network; a brief history of artificial intelligence and neural networks, and reviews open research problems in deep learning and connectionism.  Entry requirements include 90 credits in statistics and a course in linear algebra.

Natural language Processing (NLP) is one of the most important technologies in Artificial Intelligence. NLP aims at enabling computers to understand human languages and communicate with humans. There are a large variety of tasks and machine learning methods in NLP. The course provides a thorough introduction to NLP, from its history to recent advances in deep learning applied to NLP. On the task side, we will cover sequence tagging, parsing, classification and clustering, and some applications such as machine translation. On the model side, we will cover statistical models and neural networks. By learning from lectures and programming assignments, students will master necessary knowledge about NLP and engineering tricks for practical NLP problem.