Computer Science

In this course, you will create a graphical action game in Python. In the process, you will learn fundamental concepts and tools that programmers use. The course will guide you step by step from a first prototype to a working game. By the end of the course, you will deploy your game to a live website. No previous programming knowledge is required.

In this course, the fundamentals of Python are covered, with a special focus on the skills necessary for in-depth data analyses and data visualization. These two skills are fundamental in a wide range of disciplines, including but not limited to STEM (Sciences, Technology, Engineering and Mathematics) and Humanities fields of study. This course will cover the following: data types and compound data structures, conditional statements and loops, Python functions, importing, exporting and analyzing different types of data using pandas, visualizing data using Matplotlib and Seaborn, and developing interactive plots with Plotly. At the end of the two weeks course, students will work and present a final personal data analytics and visualization project.

The course covers several current and advanced topics in optimization, with an emphasis on efficient algorithms for solving large scale data-driven inference problems. Topics include first and second order methods, stochastic gradient type approaches and duality principles. Many relevant examples in statistical learning and machine learning are covered in detail. The algorithms uses the Python programming language. The course requires students to take prerequisites.

This data science course introduces essential techniques and tools used in Data Science. Each week the course covers a unique topic, starting from the very basics of the Data Science pipeline to advanced topics like Neural Networks and Time-Series Analysis, all explained using a sophisticated slides and easy-to-understand Python codes.
 

For this semester, the course will use a single comprehensive dataset that could cover all of the topics, to make it easier for students to understand the concepts of data science, without spending too much time understanding the dataset.
 

By the end of this course, students are expected to:

1.  Understand the Data Science Pipeline.
2. Apply various machine learning techniques.
3. Evaluate model performance and fine-tune hyperparameters.
4. Understand and apply Neural Networks, Text Mining, and Time-Series Analysis.
5. Translate theory into practice using Python.

Motion planning is a fundamental building block for autonomous systems, with applications in robotics, industrial automation, and autonomous driving. After completion of the course, students will have a detailed understanding of: Formalization of geometric, kinodynamic, and optimal motion planning; Sampling-based approaches: Rapidly-exploring random trees (RRT), probabilistic roadmaps (PRM), and variants; Search-based approaches: State-lattice based A* and variants; Optimization-based approaches: Differential Flatness and Sequential convex programming (SCP); The theoretical properties relevant to these algorithms (completeness, optimality, and complexity). Students will be able to: Decide (theoretically and empirically) which algorithm(s) to use for a given problem; Implement (basic versions) of the algorithms themselves; • Use current academic and industrial tools such as the Open Motion Planning Library (OMPL).

It provides a unified perspective on motion planning and includes topics from different research and industry communities. The goal is not only to learn the foundations and theory of currently used approaches, but also to be able to pick and compare the different methods for specific motion planning needs. An important emphasis is the consideration of both geometric and kinodynamic motion planning for the major algorithm types.

This course explores the truthfulness of AI, as a non-reliable source of information, from a linguistic angle. On the premise that AI-tools are increasingly used to provide “information” in professional and private settings, but in reality are producing ‘hallucinations’, false information, the course compares the logic and architecture behind large language models (LLM) used in AI-tools with the logic and architecture behind human cognition (including the capacity for language). It also delves into several aspects of human language that contribute to our inclination to take AI-generated output at face value.

This course covers the foundations and growing applications of Web computing, ranging from web crawling, search, and mining to recent trends in natural language processing on the Web. The course is designed to help students understand the fundamental notions and software technologies underlying Web information services. Topics covered: frontiers in web computing; natural language processing for text processing; foundations of information retrieval; advances in information retrieval; information extraction from documents; from information extraction to knowledge acquisition; social network analysis and recommendation systems, and web service mashups. 

This course introduces students to several computer intensive statistical methods and the topics include: empirical distribution and plug-in principle, general algorithm of bootstrap method, bootstrap estimates of standard deviation and bias, jack-knife method, bootstrap confidence intervals, the empirical likelihood for the mean and parameters defined by simple estimating function, Wilks theorem, and EL confidence intervals, missing data, EM algorithm, and Markov Chain Monte Carlo methods. This course has a prerequisite of Mathematical Statistics. 

In this course, students learn how images are formed, how they are represented on computers, and how they can be processed by computers to extract semantic information. Students develop algorithms for detecting interesting features in images, design neural networks to perform natural image classification, and explore algorithms for solving real-world problems such as hand-written digit recognition and object detection.  

This course examines various aspects of data processing including database management, representation and analysis of data, information retrieval, visualization and reporting, and cloud computing.