Computer Science

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.

This course offers a study of the basic concepts of computer architecture and the impact on performance of applications and computer systems.

Pre-requisites: Programming, Computer structure, Operating Systems 

This course covers the theories of modern deep learning and provides a practical opportunity to implement necessary deep neural network modules.

This course provides an introduction to human-computer interaction, specifically quantitative approaches to human-computer interaction research. It looks at what problems may arise in the process and how to solve those problems. It also explores how user studies are designed, conducted, analyzed, and reported.

This course introduces the design and implementation of fundamental data structures and algorithms. Topics include basic data structures (linked lists, stacks, queues, hash tables, binary heaps, trees, and graphs), searching and sorting algorithms, basic analysis of algorithms, and basic object-oriented programming concepts. The course requires students to take prerequisites.