Artificial Intelligence, M.S.

Program Curriculum

Core Courses (12 credits)

The program begins with foundational courses covering essential AI topics, such as machine learning, computer vision, and data mining.

These courses provide a solid understanding of AI’s fundamental principles and algorithms.

COSC 4550/5550 Introduction to Artificial Intelligence

COSC 4555/5555 Machine Learning

COSC 4557/5557 Practical Machine Learning

COSC 4570/STAT 4240/5240 Data Mining.

Elective Courses(12 credits for Plan A and 16 credits for Plan B).

Students can choose from various elective courses based on their interests and career goals. These courses may include specialized topics like deep learning, natural language processing, reinforcement learning, neural networks, robotics, AI ethics, AI in healthcare, AI for business, and intelligent agents. Electives allow students to deepen their knowledge in specific areas of AI that align with their research or professional interests.

PHIL 5440 Topics in the Philosophy of Mind

COSC 5560 Modern Robots and Softbots

Research Projects for Plan B Students:

Throughout the Plan B degree program, students are involved in research projects supervised by faculty members or industry experts. These projects provide hands-on experience designing and implementing AI systems, conducting experiments, analyzing data, and addressing real-world AI challenges. Research projects often culminate in a final research paper.

Seminars (2 credits)

Regular seminars and workshops will be organized to expose students to the latest research advancements, emerging trends, and challenges in AI. Experts from academia, industry, and government will deliver talks and engage in discussions, allowing students to broaden their perspectives and stay updated with the evolving AI landscape. The SoC and EECS will host, cohost, or support tech talks, colloquia, or speaker series with discipline-specific and broad AI foci.

Year 1: Fall

• COSC 4550 - Introduction to Artificial Intelligence Credits: 3

• OR

• COMP 5300 Basic Computing II. Credits: 3; The overarching goal is that students understand how to use basic computational and digital tools and approaches to solve problems across scientific, social, and human domains. This course will allow students to explore or embed their desired contextual disciplines into the foundational concepts of the School of Computing. (This is not an established UW course yet; it is proposed for development in the SoC post-baccalaureate graduate certificate program).

• COSC 4555 - Machine Learning Credits: 3

• OR

• COSC 4557 - Practical Machine Learning Credits:
• STAT 4270 - Applied Bayesian Statistics Credits: 3

• OR

• STAT 5280 - Models for Hierarchical Data Credits: 3

• OR

• STAT 5380 - Bayesian Data Analysis Credits: 3

Year 1: Spring

• STAT 4240 - Data Mining Credits: 3

• Natural Language Processing (NLP). Credits: 3; Text preprocessing and tokenization, language modeling and syntactic analysis, named entity recognition and sentiment analysis, machine translation and question answering.

• OR

• COSC 5220 - Languages and Automata Credits: 3
• COSC 5540 - Computer Vision Credits: 3

Year 2: Fall

• Deep Learning. 3 credits. Topics include neural networks and their architectures, convolutional neural networks (CNNs) for computer vision, recurrent neural networks (RNNs) for sequential data, and deep reinforcement learning

• OR

• EE 5410 - Neural and Fuzzy Systems Credits: 3

• OR

• EE 5440 - Geometric/Deep Computer Vision Credits: 3
• COSC 5552 - Advanced Topics in AI Credits: 1-3

• OR

• COMP 5350 Advanced Computing II. Credits: 3; Students will learn how to use the digital tools available in their fields of study as well as understand the theory of how digital approaches and computational methods will change their fields in the future. This course allows for the depth of knowledge within any discipline to be computationally driven towards competency and fluency. (This is not an established UW course yet; it is proposed for development in the SoC post baccalaureate graduate certificate program)
• Other graduate electives with focus on AI or application of AI OR COSC.

• OR

• Seminars and Workshops. 2 Credits

Year 2: Spring

• COSC 5960 - Thesis Research Credits: 1-12
• Other graduate electives with a focus on AI or the application of AI OR COSC. Seminars and Workshops. 2 Credits.