Computer Science

Computational Thinking is a process of solving problems typically with four steps—decomposition, pattern recognition, abstraction, and algorithmic thinking. This course concentrates on algorithmic thinking and examines how to reformulate problems with step-by-step procedures to solve the problems. Students then practice the implementation of the procedures with Python programming language in their homework assignments. This course also covers various paradigms in designing the procedures such as divide-and-conquer, greedy methods, dynamic programming, backtracking, branch-and-bound, etc., along with fundamental data structures such as linked-lists, stacks, queues, recursion, graphs, trees, binary heaps, and hashing. 

Companies such as Amazon, Airbnb, and LinkedIn build and manage powerful supply networks to create value. This course provides an understanding of these networks and their relationships with customers as well as suppliers. The digitization and innovation processes that govern these relationships are also examined. Students critically evaluate cutting-edge thinking on these topics and discuss implications for supply chain management, strategy, and marketing.

This course provides a complete understanding of basic programming concepts and how to implement them in C Sharp (C#). The course emphasizes the major features of the programming languages to solve problems in engineering. This course includes lab sessions which followed by lectures.

This course deals with a series of recent issues in artificial intelligence (AI) focusing on the field of design, more specifically deep learning and architectural space design, for beginners. Students review the related technologies with cases, and the conceptual and intellectual issues on top of AI in the perspective of design. Not only focusing on the AI techs, but also surveying the qualitative/quantitative aspects of design with theoretical issues outside of the conventional state of knowledge are the objectives of this course, empowered by actual individual project developments. Theory lectures, case studies, survey on the references, and students’ participation in class are the materials for the course. In the technological standpoint, recent decade has marked a huge change in how we perceive and talk about general AI. Buzz words “Big Data” and “Machine Intelligence” also changes (or will change) the fundamental role of designers form conventional approaches, and we will take a look where to go via this course. The deep learning (DL) techniques, for example, have shown how end-to-end differentiable functions can be learned to solve complex design tasks involving high-level perception abilities. In association with this shift and effect to our domain-specific knowledge, design, we would keep eyes opening so that we can take max advantages from it.

The course examines computer architecture, memory management, machine and assembly language and computer programming design. Other course topics include: data representations; instruction sets; machine and assembly languages; basic logic design and integrated devices; the central processing unit and its control; memory and caches; I/O and storage systems; computer arithmetic. 

This course presents the theoretical and computational foundations of brain-inspired artificial intelligence. The focus is on machine learning based on artificial neural networks, from simple models up to state-of-the-art deep learning models. The final part of the course introduces the use of neural networks as models of perception and cognition. Laboratory classes introduce students to computer simulations with artificial neural networks. The course discusses topics including artificial neural networks: mathematical formalism and general principles; supervised learning: perceptron, delta rule, multi-layered networks, and error backpropagation; generalization and overfitting; supervised deep learning; recurrent networks; unsupervised learning: associative memories and Hopfield networks, latent variable models, and Boltzmann machines; unsupervised deep learning; reinforcement learning; computer simulation as a research method in cognitive science; and connectionist models of perception and cognition. This course requires basic knowledge of mathematics (high school level), including notions of linear algebra, calculus, and probability, as well as knowledge of statistics and neuroscience as prerequisites for the course. Computer literacy is required for the lab practices.

This course focuses on the applications of machine learning algorithms to real-world questions. The overall aim is to provide theories, techniques, tools, and practical experience for applying machine learning to tackle data science problems. The course lectures cover five parts: essential concepts and techniques of machine learning, classification, regression, and clustering; application - outlier detection; application - predictive process mining; application - natural language processing; and application - reinforcement learning. For each of the four application areas, students work in a team to conduct an assignment that applies machine learning algorithms to a real-world dataset.