Masters Level Courses
AI 201 Artificial Intelligence. Fundamental concepts in Artificial Intelligence. Prerequisite: None. Credit: 3 units.
AI 211 Computational Linear Algebra for AI. Mathematical and Computational Methods for AI. Prerequisite: COI. Credit: 3 units.
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AI 212 Probability and Statistics for AI. Advanced Statistical Techniques and Models, Convergence and Inequalities, Random Number Theory for AI problems. Prerequisite: COI. Credit: 3 units.
AI 221 Classical Machine Learning. Classical supervised and unsupervised learning models for AI and engineering problems. Prerequisite: AI 211 Computational Linear Algebra for AI/equivalent/COI. Credit: 3 units.
AI 222 Advanced Machine Learning. State-of-the-art supervised, unsupervised and generative learning models for AI and engineering problems. Prerequisite: AI 221 Classical Machine Learning/equivalent/COI. Credit: 3 units.
AI 231 Machine Learning Operations (MLOps). Concepts and frameworks for collaboration and communication between machine learning engineers and operations professionals for the deployment and management of AI systems. Prerequisite: COI. Credit: 3 units.
AI 296 Graduate Seminar. Seminar or reading course on recent work in developing concepts, tools, and methods in Machine Learning, and Artificial Intelligence. Prerequisite: None. Stipulation: Must have passed 18 graduate units. Credit: 1 unit.
AI 297 Special Topics. State-of-the-art and upcoming concepts and frameworks of AI. Prerequisite: COI. Credit: 3 units.
AI 299 Capstone Project in AI. Applied research work on an approved project topic. Prerequisite: AI 231 Machine Learning Operations (MLOps). Credit: 3 units.
Doctorate Level Courses
AI 301 Computational Learning Theory. Computational and Mathematical Frameworks for Quantifying Complexity of Learning Tasks and AI Algorithms
Prerequisite: AI 212 Probability and Statistics for AI/equivalent Credit: 3 units.
AI 321 Machine Learning for Sequential Data. Design and development of applications of various machine learning algorithms on predictive maintenance and energy sequential data
Prerequisite: AI 221 Classical Machine Learning/equivalent Credit: 3 units.
AI 322 Reinforcement Learning. Fundamental concepts and principles in Reinforcement Learning and Engineering applications
Prerequisite: AI 222 Advanced Machine Learning/equivalent Credit: 3 units.
AI 341 Differential Digital Signal Processing. Theory, algorithms and applications of discrete-time signal processing systems and its applications in speech and audio signal processing
Prerequisite: AI 222 Advanced Machine Learning/equivalent Credit: 3 units.
AI 351 Machine Learning in Natural Language Processing. Machine learning approaches and state-of-the-art research in natural and statistical language processing
Prerequisite: AI 221 Classical Machine Learning/equivalent Credit: 3 units.
AI 361 Autonomous Robots. Concepts, theories, implementations and applications of autonomous robots in the human environment
Prerequisite: AI 222 Advanced Machine Learning/equivalent Credit: 3 units.
AI 362 Artificial Intelligence Accelerator. Software and hardware concepts, theories, algorithms, implementations and applications for accelerating artificial intelligence model training and inference.
Prerequisite: AI 222 Advanced Machine Learning/equivalent Credit: 3 units.
AI 380 Probabilistic Graphical Models. Learning and inference in Directed and Undirected Graphical models and their engineering applications
Prerequisite: AI 212 Probability and Statistics for AI/equivalent Credit: 3 units.
AI 390 Independent Study. Independent study and or research on a particular AI topic of interest
Stipulation: May be taken up to three times, provided the subject titles are different Credit: 3 units.
AI 397 Special Topics. State-of-the-art and upcoming concepts and frameworks of AI Stipulation: May be taken up to three times, provided the subject titles are different Credit: 3 units.
AI 400 PhD Dissertation. Research work on an approved dissertation topic; paper writing, submission, and/or revisions; Dissertation defense