Computer Science

The lecture covers four major aspects of HCI: 1. Understanding users (user behavior, user research techniques such as interviews and usability testing) 2. Designing user interfaces (principles of interface design for usability, interaction paradigms) 3. Evaluating interfaces (usability testing methods, identifying usability problems, iterative design based on user feedback) 4. Integrating HCI into system development (integrating the above aspects into an iterative product development cycle). The exercise section of the course applies the above theory in practice. Learning outcomes include: Apply HCI principles to design user-friendly interfaces; conduct fundamental user research and analyze user needs; understand principles of iterative prototyping and evaluation of interactive systems; communicate HCI concepts effectively.

This course covers fundamental concepts in various computer vision topics related to robotics, examining approaches and solutions in visual recognition problems for robots. Topics include 3D environment modeling/3D reconstruction, and object detection, recognition, and tracking using deep learning. 

All students must complete an individual project on a related topic. Suggested prerequisites: Linear algebra and probability theory, programming skills. 

This course examines intelligent agents, search algorithms, knowledge representation, machine learning, and probabilistic reasoning.

This course offers an understanding of techniques in computer systems with a focus on correctness and adherence to system properties, such as modularity and atomicity, while at the same time achieving high performance. It highlights various system mechanisms, especially from distributed systems, database systems, and network systems. Topics include system abstractions and design principles; modularity with clients and services; performance; atomicity and transactions; concurrency control and recovery; reliability, fault-tolerance, and redundancy; distributed protocols for replication; and large-scale data processing. Prerequisites include basic principles of operating systems and/or databases and working knowledge of a standard programming language (Java, C#), including concurrency and communication mechanisms.
 

This course seeks to immerse students in a professional work environment. Students have the opportunity to observe and interact with co-workers, and learn how to recognize and respond to cultural differences. Students compare concepts of teamwork and interpersonal interactions in different cultures as experienced on the job. Seminar work helps students apply academic knowledge in a business setting and identify opportunities to create value within the company. Students research a specific topic related to their work placement and present their findings in a final research report.

This course is an introduction to programming in Python, with focus on data processing and analysis. It includes basic programming concepts such as data types, conditionals, loops, functions, object oriented programming, pattern matching (regular expressions), and computational complexity. In addition, it also provides technical skills relevant to the data science pipeline such as the ability to log on to an external server, and to navigate a Unix shell. This is an introductory programming course: no prior programming experience is required. 

This course addresses one of the most important contemporary issues - responsible data use. The concept of responsible data is based on understanding the individual and societal collective duty to prioritize and respond to the ethical, legal, and social challenges coming from the use of data. The key elements of responsible data use - data privacy, data protection, and data ethics - are discussed in detail. The main feature of the course is to bring all these three elements together and to discuss them in the context of the contemporary legal and technological environment as well as future development.

The context and behaviors of computer usage have been rapidly changing as the shift of computer use environments moved from desktop computers to mobile devices to the Internet of Things, leading to the remarkable appearance of Web 3.0 technologies including XR/AI/Sensor/Blockchain which have been called new design frameworks of NPD processes that may support the big step from “interacting with computers” to “interacting with AI.”  

Now advanced enterprise architectures are rapidly adopting the “Cognitive Internet of Things.” This drastic shift signifies a fundamental game changer for the UX matters.  

This course converts the initiative in the design management strategy into the study of new HCI/UX design and analysis methods in relation to cognitive science theories and design methodologies, bridging contemporary and in-depth academic interests and approaches in the field. 

Students will explore the theoretical framework of human-computer interaction (HCI) and will build an understanding of HCI-based UX research methodology, the user research process, and practical methodologies, and will engage with the current topics of HCI/UX research and the practical use of convergence studies. 

This introductory Python algorithms course is designed for beginners in Python programming. Conducted online, this course excludes difficult mathematics and complex code, allowing students to directly code and debug basic algorithms. The course will be conducted slowly and in detail to accommodate coding novices. 

This course examines major topics in pattern recognition, particularly aspects of classification and decision. Students will gain effective pattern recognition tools with which to analyze the often vast amounts of diverse data in research applications. 

Topics include introduction to pattern recognition - machine perception - PR systems and design cycle, Bayesian decision theory for continuous features - Bayes Decision Rule - minimum-error-rate classification - classifiers, normal density, discriminant functions and discrete Bayesian decision theory - discriminant functions for the normal density - error probabilities and integrals - Bayes Decision Theory for discrete features, maximum-likelihood and Bayesian parameter estimation - Bayesian parameter estimation: Gaussian case - Bayesian parameter estimation: general theory - HMM, nonparametric techniques - density estimation - Parzen windows - nearest neighbor estimation (NN, k-NN) - fuzzy classification, linear discriminant functions i - linear discriminant functions and decision surfaces - generalized linear discriminant functions - minimizing the perceptron criterion function, relaxation procedures, linear discriminant functions ii - minimum square-error procedures - relation to Fishers linear discriminant - the Widrow-Hoff and Ho-Kashyap procedures - multicategory generalizations - ridge regression and its dual form [2] - classification error based method [2], model assessment and performance evaluation - bias, variance and model complexity [2] - model assessment and selection 2] - confusion matrix, error rates, and ROC [2] - statistical inference [2] - statistical errors [2], dimension reduction and feature extraction - principal component analysis - Fisher linear discriminant - nonlinear projections, support vector machines - introduction - SVM for pattern recognition [2] - linear support vector machines [2] - nonlinear support vector machines [2], multilayer neural networks - introduction - feedforward operation and classification - backpropagation algorithm - some issues in training neural networks - key ideas in classification, introduction to deep learning networks - convolutional neural networks (CNN) - autoencoders - deep belief networks - deep reinforcement learning - generative adversarial networks (GAN), algorithm-independent machine learning - introduction - bias and variance - resampling for classifier design - estimating and comparing classifiers - combining classifiers, unsupervised learning and clustering - mixture densities and identifiability - maximum-likelihood estimates - application to normal mixtures. 

Prerequisites: Linear Algebra, Probability, MATLAB, Python, or C-Programming Skills