The big History of Ideas & Innovation
• The evolution of human abilities through scientific and technological advancements, from the Scientific Revolution to digital innovation.
• How digital technologies, such as big data, social media and machine learning, have transformed society. Understanding the opportunities presented by ongoing innovation and disruption.
• Exploration of future possibilities with medical technologies and their ethical implications across different social levels.
• Envisioning the potential for human augmentation, including cyborgs and superhuman abilities.
Learning to Observe, Experiment & Survey
• Overview of research methods, including experimental, correlational, case study and survey approaches. Understanding scientific inquiry phases and their importance in empirical research.
• Hands-on experience in conducting experiments, using data analysis software and performing a small field study.
• Ethical principles in psychological research with human and non-human participants.
• Application of research concepts to realworld fields like behavioral economics, digital marketing and technology.
Introduction to Business Management
• Analytical frameworks companies can use to make informed decisions that lead to higher levels of success.
• Organization of companies, including the various areas involved in creating value.
• Essential personality traits that a leader must possess to effectively guide a company.
• The functions of different organizational systems and the functional areas of a company, with a focus on essential departments such as finance and marketing.
• How to internationalize an enterprise as a strategy for developing a more robust, diverse and stable business model.
Calculus
• Focus on calculus as a foundational branch of mathematics, providing a stepping stone toward formal and abstract reasoning.
• Introduction to core techniques in limits, derivatives, and integrals.
• Exploration of calculus applications in algorithm complexity and key areas of computer science.
• Combination of pure mathematical thinking with computational problemsolving.
IE Humanities
• Foundations for conducting profound analyses of the world we live in.
• Using critical thinking to dissect others’ ideas and effectively defend your own ideas.
• Focus on the knowledge necessary for comprehending our surroundings as well as the skills required to construct and articulate ideas.
• Diverse range of Humanities topics to choose from: History, Art History, Literature, Philosophy, Intellectual History, Theoretical Linguistics, Applied Science, Communication Theory, Musicology and Film Studies.
• Problem-solving classes and critical discussion sessions, utilizing methods such as the case method, multimedia cases and virtual reality cases.
Principles of Programming
• Introduction to essential programming tools, using C and Bash to build a foundation in coding, file navigation and debugging without graphical tools.
• Skill-building in C programming, progressing from basic to complex programs to master key concepts and features unique to the language.
• Advanced programming concepts, applying multiple learned techniques in complex practical projects, with theoretical lessons to support continued growth in programming.
Physics for Computer Science
• Introduction to the basic concepts for understanding the physics behind computer science.
• Fundamentals of mechanical systems and electromagnetic forces as they relate to computer science.
• Introduction to electric circuit analysis and its applications.
• Basics of semiconductor models and transistor design for representing formal logic in computing.
Fundamentals of Data Analysis
• Probability models and statistical methods for gaining insights into the behavior of various phenomena and making informed decisions in the presence of uncertainty and variation.
• Use of data analysis techniques for experiments to reduce variation and improve the accuracy of conclusions.
• Learning to make statistical inferences, including confidence intervals, hypothesis testing and analysis of variance for one or more factors.
• Foundation for advanced topics such as Algorithms and Data Structures, Probability for Computer Science and AI: Statistical Learning and Prediction.
Simulating and Modeling to Understand Change
• Introduction to simulation and modeling as tools for exploring physical phenomena and systems without the need for real-world experimentation.
• Advantages of simulation, including greater realism, speed and the ability to study complex systems that emerge from smaller individual elements.
• Applications of simulation across various scientific fields such as mathematics, physics, engineering, psychology and biology.
• In-depth coverage of Monte Carlo simulation, discrete-event simulation, model building and regression and classification models.
• Practical skills in statistics and programming to conduct simulation studies and model real-life scenarios.
Physics for Computer Science Lab
• Fundamental principles of mechanics, including gravity, kinematics and conservation laws.
• Basics of electromagnetism, focusing on electrostatics, capacitors, circuits and digital electronics.
• Logic circuits, with hands-on use of transistors for logical design and timedependent circuits.
• Practical experience that complements the theoretical Physics for Computer Science course.
• Foundational understanding essential for computer science applications in mechanics, electromagnetism and electronics.
Technology
• Studying technologies that address societal challenges, such as the internet of things, artificial intelligence, data science, blockchain and cloud computing.
• The impact of these technologies on society, analyzing potential applications and limitations.
• Discussion of ethical issues, including concerns about bias, security and privacy.
• Understanding how transformative technologies reshape interactions at social, enterprise and individual levels, equipping students to critically assess technological applications and risks.
Alculus for Computer Science
• The historical connection between mathematics and computer science and the influence of mathematicians like Gödel, Turing and von Neumann.
• The ongoing exchange of ideas between mathematics and computer science that has shaped the field from its inception to today.
• Integration of pure mathematical thinking with practical problem-solving using computers.
Computer Programming I
• Taking C programming skills to the next level.
• Advanced C programming concepts and techniques to ensure efficient, portable and high-quality code.
• Calculus as a foundation for mathematical, formal and abstract reasoning, focusing on limits, derivatives and integrals. Exploration of its applications in computer science, particularly in algorithm complexity and other key topics.
• Learning how to build multithreaded programs capable of communicating over the internet.
Designing and Using Databases
• Introduction to the importance of databases in technology projects, preparing students to collaborate with database designers and programmers in a corporate setting.
• Exploration of various database types, from traditional business intelligence systems to advanced solutions for webscale applications.
• Hands-on experience with structured databases, SQL, NoSQL and Big Data technologies, including creating documentation, diagrams and implementing databases.
• Focus on skills to design and implement innovative database systems for modern, dynamic organizations.
Algorithms & Data Structures
• Introduction to algorithms as computational procedures that transform inputs into outputs to solve computational problems.
• Understanding a variety of data structures and algorithms for solving computational problems effectively.
• Learning how to compare and choose the best data structure or algorithm for a given problem.
• Emphasis on practice and group work to solidify concepts and ensure mastery of algorithms and data structures.
Cloud Computing
• Overview of cloud computing and its benefits, including scalability, elasticity and cost savings through a consumptionbased model.
• Practical focus on cloud computing for computer scientists, covering business, architectural and hands-on aspects of small to medium-sized cloud projects.
• Studying cloud service models (SaaS, PaaS, IaaS), with hands-on experience using Microsoft Azure and AWS to demonstrate concepts and design patterns.
• Introduction to enabling technologies such as Virtualization, Containers and Automation for Linux systems.
• Group project implementing end-to-end cloud architecture, allowing students to apply concepts to real-world scenarios.
SPRING SEMESTER
Entrepreneurship
• Fundamental principles of entrepreneurship, encompassing both macro and micro perspectives.
• Three core pillars: creativity, logical reasoning and problem-solving skills. The foundation for entrepreneurs to navigate the complexities of the contemporary business landscape.
• Addressing broader global goals, such as climate and environmental challenges, sustainable energy transition, promoting One Health initiatives, advocating for sustainable cities and nurturing green job opportunities.
• Focus on entrepreneurial skills, knowledge and mindset required to contribute to broader societal and environmental objectives.
Matrices & Linear Transformations
• Overview of linear algebra concepts, focusing on matrices and linear transformations and their applications in fields like computer science, business, physics, economics and engineering.
• Emphasis on the importance of understanding algorithms and interpreting results rather than just performing calculations.
• Practical application of foundational concepts in vectors and matrices, revisiting and applying them to real-world problems.
• Development of mathematical intuition to enable students to abstract linear algebra concepts and use them for problemsolving in their careers.
Probability for Computer Science
• Introduction to the fundamental concepts and applications of probability theory in the field of computing.
• Concepts such as Markov chains, Poisson processes, Hidden Markov models (HMMs) and Bayesian networks, which are crucial for understanding and solving problems related to algorithms, machine learning, data science and artificial intelligence.
• Combining theoretical foundations and practical applications in order to apply probabilistic methods to complex computing problems.
Computer Architecture, Network Technology & Operating Systems
• Introduction to the main elements of computer architecture.
• Learning about the evolution of operating systems over the last fifty years and how to operate within them.
• Studying the major components of most modern operating systems, as well as the fundamentals of networks and the breakdown of the layers involved.
• Architectural elements of modern operating systems and processors and their impact on performance.
AI: Machine Learning Foundations
• Overview of AI’s evolution into a mainstream driver of innovation, with its increasing impact on labor productivity and global economic value.
• Focus on machine learning (ML) as a key component of AI, emphasizing its ability to solve complex problems by learning from data and adapting through experience.
• Exploration of ML’s interdisciplinary nature, drawing from fields like computer science, statistics and neuroscience and its rapid progress in both theory and practical applications.
• Hands-on learning of essential ML algorithms, preparing students to apply them to real-world challenges in various industries.
AI: Personality and Emotion for AI Design
• Exploration of how psychological processes like personality and emotion shape human behavior and perception and their importance in developing AI systems that interact naturally with humans.
• Study of how research in psychology contributes to creating more human-like AI by modeling cognition and emotion.
• Application of mathematical and computational psychology tools to better understand and simulate human psychological processes in AI design.
• Development of key principles for improving human-machine interaction and generating more human-like behaviors in AI systems.
FALL SEMESTER
High Performance Computing
• Introduction to high-performance computing (HPC), covering parallel coding, distributed systems and applications in machine learning and deep learning.
• Practical applications in health, neuroscience and scientific fields, with hands-on experience using tools like MPI, OpenMP, GPU computing and cloudbased HPC solutions.
• Emphasis on developing efficient HPC applications, parallel algorithms and scalability, particularly for distributed machine learning and deep learning.
• Exploration of advanced HPC topics, such as neuromorphic and quantum computing, preparing students to tackle challenges in artificial intelligence and related fields.
Computer Programming II
• Comprehensive course in C++, a language essential in performance-critical areas like game development, systems programming, high-frequency trading and embedded systems. Introduction to Object-oriented programming (OOP).
• Fundamental concepts and advanced features like templates, the Standard Template Library (STL) and memory management, with a focus on software engineering best practices.
• Hands-on learning through individual presentations and a group project, allowing students to apply theory to realworld problems.
Software Development & Devops
• Introduction to the discipline of software development and modern DevOps methodologies, including software development life cycle and methodologies.
• Software architectural patterns and design principles, covering all the phases in the software life cycle.
• DevOps frameworks, principles, practices and tooling and their relationship with agile methodologies. Implementation strategies for start-ups and large organizations, including adoption and change management.
• Practical project: Design, develop and release a software feature following DevOps methodology.
AI: Reasoning & Problem Solving
• Introduction to artificial intelligence (AI) covering problem representation, search algorithms and reasoning agents. Topics include intelligent agents, search techniques, constraint satisfaction problems, adversarial search, games and knowledge reasoning.
• Applying knowledge through Python coding, solving problems like search issues and adversarial games (e.g. chess, poker). Hands-on experience in Python, including visualizing search algorithms, solving real-world problems and creating agents for decision-making and games.
• Emphasis on developing theoretical understanding of AI problem representation and practical application of search and reasoning strategies.
IE Challenge
• An opportunity for students to enhance their proficiency in employing innovative methodologies, such as Design Thinking and Lean Startup, to address genuine challenges.
• Developing an understanding of and empathy for unfamiliar realities, while nurturing a sensitivity towards social issues aligned with the Sustainable Development Goals.
• Building skills to navigate the intricacies of launching and executing projects within complex contexts.
• Developing the ability to discern customer needs and create value that generates a positive impact on their organization and its mission.
• Critical discussion sessions and debates regarding business models.
• Practical classes in collaboration with selected startups enable students to work as a team.
AI: Statistical Learning & Prediction
• Statistical learning, prediction and artificial neural networks, covering both classic machine learning methods and advanced techniques in deep learning and generative AI.
• Using diverse datasets and hands-on assignments to build experience and intuition with various data types.
• Applications covered will include image classification, image captioning, speech recognition, text generation and image synthesis.
• Programming in Python, with coding demonstrations throughout the course to reinforce theoretical concepts and enhance practical skills.
AI: Natural Language Processing & Semantic Analysis
• Exploration of the transformative field of Natural Language Processing (NLP), focusing on its role in reshaping datadriven decision-making and human communication.
• Ways that NLP and Text Analysis, including applications like ChatGPT and Bard, are revolutionizing industries and driving innovations.
• Building the skills to leverage NLP within data science, using powerful tools and techniques to extract insights from vast data sets and make a meaningful impact.
AI: Chatbots & Recommendation Engines
• Understanding how recommendation engines and chatbots enhance user experiences, lower costs and facilitate product discovery in businesses.
• Exploring the role of large language models (LLMs) like ChatGPT and Bard in transforming interactive search and customer interaction.
• Practical insights into recommender systems and chatbots, with detailed examples of their applications in industry.
AI: Reinforcement Learning
• Key components of reinforcement learning (RL), which combines dynamic programming, heuristic search and advanced neural network techniques to create a scalable discipline.
• Exploring various approaches, ranging from basic Q-Value models to advanced techniques like Deep Q-Learning, policy gradients, actor-critic methods and curiosity-driven innovations.
• Discussing potential future trends that could lead to advancements in this field.
AI: Computer Vision
• Introduction to the most relevant topics in computer vision, a crucial engineering field that seeks to achieve a high-level understanding of the world through digital images and videos.
• Exploration of traditional computer vision techniques as well as modern approaches relying on machine learning.
• Innovative applications of computer vision in industrial automation, autonomous vehicles, precision agriculture and e-health.
Ethics, Policy Making and Legislation In CS
• Exploring ethical, regulatory and legal issues in computer science, preparing students to address dilemmas in technology development.
• Analyzing case studies and participating in debates to develop critical-thinking and ethical analysis skills.
• Topics such as privacy, security, accountability, intellectual property, sustainability, equality and autonomy, all grounded in moral philosophy and international laws.
• Building the knowledge to make informed, ethical decisions in their technology careers.
Robotics & Automation
• Fundamentals of robotics, focusing on how robots sense, plan and act to achieve specific goals.
• Understanding the theory behind robotics through lectures on key concepts and principles.
• Gaining practical experience with robot simulations using ROS (Robot Operating System), the standard software used in robotics research.
• Application of knowledge in a hands-on lab course (Robotics & Automation LAB), where learned concepts will be put into practice with real robot hardware.
