Computer Science

This course introduces the basic concepts of computer programming and problem-solving using Python, analyzing and devloping algorithms as well as developing programs, debugging, and testing of various problems. 
 

The course covers the principles and main topics of Python including variables, conditional branches, loops, functions, lists, dictionaries, recursion, file input/output, and the introduction of object-oriented programming. The course also provides opportunities to solve problems such as numerical simulations, combinatorial problems, and image processing.

This is a course for all students interested in using and understanding computers. Students learn the practical skill of how to program a computer to make it do what they want it to do. Students learn how to write simple computer programs that can solve problems; how to write simple programs that can process different sorts of information; and how to write programs that can respond differently to different situations. 

This course examines programming language design issues and programming paradigms. It covers binding and scoping, parameter passing, lambda abstraction, data abstraction, type checking, and functional and logic programming.

This course examines discrete mathematics and structures pertinent to computer science. Topics include logic; set theory; mathematical reasoning; counting techniques; discrete probability; trees, graphs, and related algorithms; modeling computation. 

This course includes prediction using machine learning; choice of features, including for text, images, time series; model selection (e.g. linear, kernel, neural net); learning as empirical risk minimization; common machine learning techniques (linear regression, logistic regression, SVMs, kernel trick, neural nets, convolutional neural nets, kNN, k-Means); evaluating machine learning methods (cross-validation, bootstrapping, ROC, use of a baseline); and practical experience of applying machine learning methods to real data.  

This course introduces the foundations of intelligent systems, such as probabilistic modeling and inference, statistical machine learning, computer vision, and robotics, to undergraduate students. Topics include Bayesian networks, hidden Markov models, Kalman filters, Markov decision processes, linear regression, linear classification, and nonparametric models. Students will also learn about how these methods are applied to practical applications such as computer vision and robotics. 

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.