Master of Science in Applied Artificial Intelligence

This course covers advanced topics in machine learning; supervised learning (linear regression, logistic regression, classification, support vector machines, kernel methods, decision tree, Bayesian methods, ensemble learning, neural networks); unsupervised learning (clustering, EM, mixture models, kernel methods, dimensionality reduction); learning theory (bias/variance trade-offs); and reinforcement learning and adaptive control.

This course delves into the critical aspects of creating AI systems that are both trustworthy and ethical. It covers the principles of ethical AI design, including transparency, fairness, privacy, and accountability. Students will explore various frameworks and guidelines for ethical AI development, discuss case studies of AI systems with significant societal impacts, and learn how to implement techniques for bias detection and mitigation. The course also addresses the importance of trust in AI, focusing on building reliable systems that earn user confidence through ethical decision-making processes. Through interactive discussions, hands-on projects, and critical analysis, participants will learn how to navigate the ethical challenges in AI development and contribute to the creation of responsible AI technologies.

Advanced deep learning concepts and natural language processing (NLP) fundamentals, Language modeling, Vector space semantics and Embeddings, Sequence labeling, Syntactic parsing, semantic analysis, Information Extraction, Machine translation, Discourse Coherence, Question Answering, Dialogue Systems and Chatbots, and Natural language summarization. The course also covers deep learning models in the context of language processing

The course will focus on algorithms used in computer vision applications while explaining the pattern recognition aspect of these algorithms. Topics include Taxonomy of computer vision tasks, applications of computer vision, image representation in the spatial and frequency domains, image formation, image filtering, feature detection and matching, image segmentation, image classification, object detection, image alignment and stitching, motion estimation and tracking, depth estimation, and deep learning for computer vision.

This course offers a comprehensive exploration of modeling in Generative AI applications, focusing on the cutting-edge Generative Pre-trained Transformer (GPT) language processing model. Students will delve into the fundamentals of natural language processing, deep learning, and the intricate mechanisms behind GPT. Throughout the course, participants will acquire hands-on experience in constructing and customizing their own GPT models for a diverse range of language processing applications.

This is an advanced course designed to explore the cutting-edge applications and methodologies of artificial intelligence across a variety of sectors. This course offers a deep dive into specialized AI domains such as autonomous systems, AI in healthcare, natural language processing advancements, and ethical AI deployment. Through a blend of lectures, case studies, and project-based learning, students will engage with the latest AI research and tools, applying their knowledge to solve real-world problems. Emphasizing innovation and critical thinking, this course prepares participants to contribute to the advancement of AI technologies and their applications, while considering the societal impacts and ethical implications of their work.

Fundamental concepts and techniques of intelligent systems. Principles and methods for heuristic search, knowledge representation, problem-solving, planning and reasoning with uncertainty, game, and adversarial search, and their application to building intelligent systems in a variety of domains. Basics of machine learning, visual perception, and natural language processing and introduction to AI programming

This course provides an overview and introduction to the field of human-computer interaction and its applications in AI-enabled systems. It introduces students to tools, techniques, and sources of information about HCI and provides a systematic approach to design. The course increases awareness of bias in data-driven AI models, good and bad design through observation of existing technology, and teaches the basic skills of task analysis, and analytic and empirical evaluation methods. Graduate students will also participate in a laboratory where they will practice HCI techniques in an independent, self-defined project.

In this course, students are divided into teams to survey the field of AI applications, make presentations to the faculty and fellow students on areas that are ripe for AI development, and develop a product proposal, which will be carried through for the semester-long term project. Students learn and build deep learning applications using TensorFlow and Python. Topics include supervised learning, feed-forward neural networks, flow graphs, dynamic computational graphs, convolutional neural networks, and recurrent neural networks. Students will use high-level tools to engineer functioning machine learning models.

Analysis is the systematic examination of an artifact to determine its properties. This course will focus on analysis of software artifacts--primarily code, but also including analysis of designs, architectures, and test suites. We will focus on functional properties, but also cover quality attributes like performance and security. In order to illustrate core analysis concepts in some depth, the course will center on static program analysis; however, the course will also include a breadth of techniques such as testing, model checking, theorem proving, dynamic analysis, and type systems. Concern for realistic and economical application of analysis will also be evident in a bias toward analyses that are scalable and incremental. The course emphasizes the fundamental similarities between analyses (in their mechanism and power) to teach the students the limitations and scope of the analyses, rather than the distinctions that arose historically (static vs. dynamic, code vs. spec). The course will balance theoretical discussions with lab exercises in which students will apply the ideas they are learning to real artifacts.

This course is designed to give an understanding of the key concepts and principles in creating and managing successful software testing for AI-enabled systems to meet specific requirements using best practices of software quality assurance. Topics covered include software quality assurance, testing process, test design & coverage techniques, and testing strategy. Best practice strategies in AI software testing are also discussed. An overview of test automation methods and tools is also covered

This course offers an in-depth exploration into the core concepts and innovative applications of Artificial Intelligence in the engineering domain. This course aims to equip learners with a robust understanding of AI fundamentals, including machine learning, neural networks, natural language processing, and robotics, alongside their practical implications in solving real-world engineering challenges. Through a balanced approach of theory and hands-on projects, participants will delve into the process of designing, developing, and deploying AI systems across diverse sectors such as healthcare, automotive, and environmental engineering. By the end of this course, students will not only grasp the theoretical underpinnings of AI but also acquire the practical skills necessary to apply AI technologies in engineering contexts, preparing them for advanced roles in the rapidly evolving AI landscape.

The course is designed to merge the fields of Artificial Intelligence and cybersecurity, providing students with insights into how AI technologies can enhance security protocols and defense mechanisms. This concise program covers the application of machine learning algorithms, anomaly detection, and AI-driven threat intelligence to predict, detect, and counteract cyber threats effectively. Participants will engage in real-world scenarios to develop AI-powered security solutions, preparing them for the challenges of safeguarding digital assets in an increasingly complex cyber landscape.

This comprehensive course on Natural Language Processing (NLP) and Large Language Models (LLMs) introduces participants to the foundational concepts and advanced techniques in the realm of machine learning that enable computers to understand, interpret, and generate human language. Through a blend of theoretical instruction and practical exercises, students will explore key topics such as text processing, sentiment analysis, language generation, and the architecture of state-of-the-art models like GPT-4. Students will gain hands-on experience in training, fine-tuning, and deploying large language models for a variety of applications, including chatbots, automated content creation, and linguistic data analysis. Emphasizing ethical considerations and the societal impacts of NLP technologies, this course equips students with the skills to leverage the power of language models responsibly and innovatively in their future projects or research.

This course delves into the pivotal role of Information Theory in Artificial Intelligence (AI) systems, offering a deep dive into how principles of data transmission, compression, and entropy underpin the efficiency and effectiveness of AI technologies. Through engaging lectures and hands-on labs, students will explore the mathematical frameworks and algorithms that facilitate machine learning models' ability to learn from data, make predictions, and improve over time. The curriculum covers essential topics such as Shannon's entropy, information gain, and mutual information, applied in the context of optimizing neural networks, enhancing data encoding schemes, and ensuring secure AI communication channels. Designed for both theorists and practitioners, this course empowers participants with the knowledge to harness information theory concepts in developing advanced AI systems, optimizing their performance, and pioneering innovative solutions in the field.

This course on Speech Recognition and Understanding bridges the gap between human language and machine processing, providing students with a comprehensive overview of the technologies that enable machines to recognize, interpret, and respond to human speech. Participants will explore the core algorithms and statistical models that power speech recognition systems, such as Hidden Markov Models (HMMs) and deep neural networks, alongside techniques for noise reduction, accent adaptation, and semantic analysis. Through practical exercises, students will learn to build and refine speech recognition models, implement natural language understanding (NLU) components, and develop applications capable of interacting with users through spoken language. Emphasizing current challenges and future directions in speech technology, this course prepares learners to innovate and lead in the rapidly evolving field of voice-enabled AI

This dynamic course on AI in Robotics introduces students to the cutting-edge intersection of artificial intelligence and robotics, equipping them with the skills to design, program, and deploy intelligent robotic systems. Through a blend of theory and hands-on projects, participants will delve into the core concepts of machine learning, computer vision, sensor integration, and autonomous decision-making, applying these principles to solve real-world robotics challenges. The curriculum covers the development of robots capable of navigating complex environments, performing tasks with precision, and learning from their interactions. By exploring contemporary case studies and engaging in collaborative projects, students will gain a comprehensive understanding of

This dynamic course offers a deep dive into AI-Driven Data Science Techniques, equipping students with the knowledge to harness the power of artificial intelligence in extracting insights, making predictions, and driving decisions from complex datasets. Covering a broad spectrum of methodologies, including machine learning algorithms, deep learning networks, and reinforcement learning, participants will learn to apply these techniques to real-world data science problems. Through hands-on projects, learners will tackle challenges in various domains such as finance, healthcare, and social media analytics, utilizing AI to uncover patterns, predict trends, and optimize outcomes. The course also emphasizes ethical considerations and the responsible use of AI in data science, preparing students to become proficient and conscientious data scientists in a technology-driven world.

Students enrolled in the thesis option prepare an MSc thesis proposal that includes the research problem(s) to be addressed by the proposed applied AI research, a thorough literature review of the related works, the objectives, the methodology to be followed, the results and contributions expected from the proposed research, as well as timeline and schedule of the proposed research. The research proposal will be evaluated according to the university regulations and college/department internal procedures.
Students are expected to write a report, referred to as a thesis, on the results of an original investigation, in conjunction with a master’s Advisory Committee. Length and style of the thesis vary by college/department. All these are filed with the Office of Graduate Studies. A Master’s Advisory Committee will be formed for each student. The Chair of the Committee must have research and graduate student advising experience. This Committee will assist the student in the formulation of the Thesis Proposal, and later advise the student in the execution of the research, the Thesis write-up, and help the student to prepare for the oral defense.

Students enrolled in the thesis option prepare an MSc thesis proposal that includes the research problem(s) to be addressed by the proposed applied AI research, a thorough literature review of the related works, the objectives, the methodology to be followed, the results and contributions expected from the proposed research, as well as timeline and schedule of the proposed research. The research proposal will be evaluated according to the university regulations and college/department internal procedures.
Students are expected to write a report, referred to as a thesis, on the results of an original investigation, in conjunction with a master’s Advisory Committee. Length and style of the thesis vary by college/department. All these are filed with the Office of Graduate Studies. A Master’s Advisory Committee will be formed for each student. The Chair of the Committee must have research and graduate student advising experience. This Committee will assist the student in the formulation of the Thesis Proposal, and later advise the student in the execution of the research, the Thesis write-up, and help the student to prepare for the oral defense.

Students enrolled in the thesis option prepare an MSc thesis proposal that includes the research problem(s) to be addressed by the proposed applied AI research, a thorough literature review of the related works, the objectives, the methodology to be followed, the results and contributions expected from the proposed research, as well as timeline and schedule of the proposed research. The research proposal will be evaluated according to the university regulations and college/department internal procedures.
Students are expected to write a report, referred to as a thesis, on the results of an original investigation, in conjunction with a master’s Advisory Committee. Length and style of the thesis vary by college/department. All these are filed with the Office of Graduate Studies. A Master’s Advisory Committee will be formed for each student. The Chair of the Committee must have research and graduate student advising experience. This Committee will assist the student in the formulation of the Thesis Proposal, and later advise the student in the execution of the research, the Thesis write-up, and help the student to prepare for the oral defense.

This course first prepares the student for the project that shall be completed by students who take the coursework (project) path. Students work closely with the supervisor to define the scope of the AI-enabled project (of appropriate complexity) and understand its requirements, identify the tools required to do the project, and review relevant related literature. The student shall submit a written report to his supervisor at the end of the semester.
Following the project preparation study, the students apply the knowledge gained throughout the program. The project can take the form of a theoretical or experimental study (analysis, evaluation, comparison, etc.) or the design and/or implementation and/or maintenance of one or more components of a system. Students write a report describing their work and perform an oral presentation in front of an examination committee.

This course first prepares the student for the project that shall be completed by students who take the coursework (project) path. Students work closely with the supervisor to define the scope of the AI-enabled project (of appropriate complexity) and understand its requirements, identify the tools required to do the project, and review relevant related literature. The student shall submit a written report to his supervisor at the end of the semester.
Following the project preparation study, the students apply the knowledge gained throughout the program. The project can take the form of a theoretical or experimental study (analysis, evaluation, comparison, etc.) or the design and/or implementation and/or maintenance of one or more components of a system. Students write a report describing their work and perform an oral presentation in front of an examination committee.

This course first prepares the student for the project that shall be completed by students who take the coursework (project) path. Students work closely with the supervisor to define the scope of the AI-enabled project (of appropriate complexity) and understand its requirements, identify the tools required to do the project, and review relevant related literature. The student shall submit a written report to his supervisor at the end of the semester.
Following the project preparation study, the students apply the knowledge gained throughout the program. The project can take the form of a theoretical or experimental study (analysis, evaluation, comparison, etc.) or the design and/or implementation and/or maintenance of one or more components of a system. Students write a report describing their work and perform an oral presentation in front of an examination committee.