Online Applied AI & Machine Learning Graduate Certificate

At a Glance

Completion Time

Months

Credits Required

Tuition

$14,850

Part-Time*

*Based on 2026–2027 Boston University tuition and fees.

Build Practical, Dynamic Artificial Intelligence Skills to Advance Your Career Across Industries

The Graduate Certificate in Applied AI & Machine Learning at Boston University’s Metropolitan College (BU MET) introduces online learners seeking industry-relevant AI skills to key topics in the field, including neural networks, generative AI, AI security, intelligent image processing, reinforcement learning, automated reasoning, and ethical considerations in AI.

Designed to prepare you for roles like machine learning engineer, AI developer, data scientist, computer vision expert, ethical AI analyst, natural language processing (NLP) specialist, and AI product manager, the curriculum emphasizes hands-on experience with real-world AI applications. Through practical projects, you’ll learn how to design, develop, and deploy intelligent systems across multiple industries, leaving you prepared to meet the growing workforce demand for AI professionals in today’s technology-driven world.

The Graduate Certificate in Applied AI & Machine Learning is also available on campus in Boston. Learn more.

Curriculum

The online Graduate Certificate in Applied AI & Machine Learning consists of four required courses (16 units).

Academic units earned toward the online Graduate Certificate in Applied AI & Machine Learning may be transferred to the Master of Science in Computer Science concentration in AI & Machine Learning. Please note that the MS in Computer Science is only offered on campus.

Courses

MET CS 577 Data Science with Python

4 credits.

Prerequisite: (MET CS 521) or equivalent or instructor's consent. Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class.

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Mohan	Independent	R	06:00:00 PM–08:45:00 PM	PHO 202

Section A4, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Independent	T	09:00:00 AM–11:45:00 AM	STH 113

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Mohan	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 664 Artificial Intelligence

4 credits.

Prerequisites: MET CS 521 or equivalent. - Study of the ideas and techniques that enable computers to behave intelligently. Search, constraint propagation, and reasoning. Knowledge representation, natural language, learning, question answering, inference, visual perception, and/or problem solving. Agent frameworks for developing modern agents are explored. Laboratory course.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Kalathur	Independent	W	06:00:00 PM–08:45:00 PM	SCI 115

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Kalathur	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 767 Advanced Machine Learning and Neural Networks

4 credits. Fall and Spring

Prerequisites: MET CS 521 and at least one of MET CS 577, MET CS 622, MET CS 673 or MET CS 682; or consent of instructor. Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Regression, k-means, KNN's, Neural Nets and Deep Learning, Transformers, Recurrent Neural Nets, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptron's, backpropagation, attention, and transformers. Each student creates a term project.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Mohan	Independent	M	06:00:00 PM–08:45:00 PM	PSY B35

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	ARR	12:00:00 AM–12:00:00 AM

And one course from the following:

MET CS 688 Web Mining and Graph Analytics

4 credits. Fall and Spring

Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. - The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms, link analysis and community detection based on graphs. Laboratory Course.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Hajiyani	Independent	T	06:00:00 PM–08:45:00 PM	CAS 228

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Vasilkoski	Independent	R	06:00:00 PM–08:45:00 PM	HAR 220

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 699 Data Mining

4 credits. Fall and Spring

Prerequisites: MET CS 521, MET LB 103 and MET LB 104; and either MET CS 579 or MET CS 669; or consent of instructor. - Study basic concepts and techniques of data mining. Topics include data preparation, classification, performance evaluation, association rule mining, regression and clustering. You will learn underlying theories of data mining algorithms in the class and practice those algorithms through assignments and a semester-long class project using R. After finishing this course, you will be able to independently perform data mining tasks to solve real-world problems.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Lee	Independent	W	06:00:00 PM–08:45:00 PM	SOC B63

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Lee	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 766 Deep Reinforcement Learning

4 credits. Fall and Spring

Prerequisites: MET CS 767 or consent of instructor. - Investigate reinforcement learning, focusing on fundamental concepts and advanced techniques. You will begin with an introduction to reinforcement learning and key concepts, such as exploitation versus exploration and Markov Decision Processes. Then, as the course progresses, you will delve into state transition diagrams, the Bellman equation, and solutions to the Multi-Armed Bandits problem. Challenges and methods for control and prediction will be explored, as well as tabular methods such as Monte Carlo, Dynamic Programming, Temporal Difference Learning, SARSA, and Q-Learning. The course culminates in a review of neural network concepts, covering convolutional and recurrent neural networks, and approximation methods for both discrete and continuous spaces, including DQN and its variants. Policy gradient methods, actor-critic methods, and ethical considerations in AI and safety issues are also discussed.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	T	06:00:00 PM–08:45:00 PM	CAS 426

MET CS 777 Big Data Analytics

4 credits. Fall and Spring

Prerequisites: (MET CS 521 & MET CS 544 & MET CS 555) or MET CS 577 or consent of instructor. An overview of the principles and practice of large-scale data analytics. You will examine methods for extracting meaningful insights from large, complex, and distributed datasets, learning about core technologies for storing and processing high-volume data. This course emphasizes distributed computing frameworks based on the MapReduce paradigm, including Hadoop MapReduce and Apache Spark, along with programming models, parallel data processing, and performance considerations in cluster-based environments. Through hands-on assignments and projects, you will implement data processing algorithms and deploy them on cloud platforms such as Amazon Web Services (AWS) and Google Cloud, developing the practical skills required for data engineering and large-scale analytics in real-world environments. Educational cloud accounts and credits are provided.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	W	06:00:00 PM–08:45:00 PM	CAS 218

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Trajanov	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 787 AI and Cybersecurity

4 credits.

Prerequisites: MET CS 577 or consent of instructor. This course provides an in-depth exploration of the critical intersection between Artificial Intelligence (AI) and cybersecurity, focusing on two interconnected themes: protecting AI systems from vulnerabilities and harnessing the power of AI to tackle cybersecurity challenges. As AI becomes a cornerstone of modern technology, ensuring the security of AI-powered systems against adversarial attacks, backdoor attacks, and model theft is essential. Simultaneously, AI offers transformative capabilities for malware detection, intrusion prevention, and malware analysis. Through a combination of theoretical foundations, hands-on exercises, and real-world case studies, students will delve into topics such as adversarial machine learning, backdoor injection and defense, IP protection, and privacy-preserving AI. They will also learn how to design and implement AI-driven tools for identifying and mitigating cyber threats in dynamic environments. The course emphasizes practical applications, encouraging students to build resilient AI systems and utilize advanced AI techniques to enhance system security and detect emerging threats. Hands-on labs based on existing tools are provided and required.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Independent	M	06:00:00 PM–08:45:00 PM	BRB 121

MET CS 788 Generative AI

4 credits. Fall

Prerequisites: MET CS 577, Python programming, mathematics required for machine learning, and familiarity with neural networks. Or consent of instructor. - The first part of the course covers statistical concepts required for generative artificial intelligence. We review regressions and optimization methods as well as traditional neural network architectures, including perceptron and multilayer perceptron. Next, we move to Convolutional Neural Networks and Recurrent Neural Networks and close this part with Attention and Transformers. The second part of the course focuses on generative neural networks. We start with traditional self-supervised learning algorithms (Self Organized Map and Restricted Boltzmann Machine), then explore Auto Encoder architectures and Generative Adversarial Networks and move toward architectures that construct generative models, including recent advances in NLP, including LLMs, and Retrieval Augmented Methods. Finally, we describe the Neural Radiance Field, 3D Gaussian Splatting, and text-2-image models.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	W	06:00:00 PM–08:45:00 PM	CAS 233

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 790 Computer Vision in AI

4 credits. Fall and Spring

Prerequisites: MET CS 566 or instructor's consent. - Students enrolled in this course will gain comprehensive insights into fundamental and advanced concepts within the dynamic realm of computer vision. The curriculum will focus on cutting-edge applications of deep neural networks in computer vision. Through hands-on experiences and practical exercises, students will learn to leverage computer vision and machine learning techniques to solve real-world challenges. This course not only equips students with theoretical knowledge but empowers them to apply these concepts effectively, fostering a deep understanding of how computer vision can be harnessed to address complex problems in diverse industries.

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Zhang	Independent	ARR	12:00:00 AM–12:00:00 AM

Dates & Deadlines

View BU MET’s academic calendar for online programs, including important dates and deadlines.

Application Requirements

Learn about application requirements for BU MET graduate degree and certificate programs.

A digital image of data points rising up to resemble a digital brain

How You Benefit from a
Boston University Education

A BU credential can help lay the foundation for career advancement and personal success.

Benefit from an average 24:1 student-to-instructor ratio.
Work closely with highly qualified faculty and industry leaders who have hands-on involvement in data analytics, data science, data storage technologies, cybersecurity, artificial intelligence (AI), machine learning, software development, and many other areas.
BU MET’s computer science courses ensure you get the attention you need, while introducing case studies and real-world projects that emphasize technical and theoretical knowledge—combining in-depth, practical experience with the critical skills needed to remain on the forefront of the information technology field.
BU MET’s Department of Computer Science was established in 1979 and is the longest-running computer science department at BU. Over the course of its existence, the department has played an important role in the emergence of IT at the University and throughout the region.

Rankings & Accreditations

U.S. News & World Report - Best Online Programs - Grad Information Technology - 2026

#12, Best Online Master’s in Computer Information Technology Programs

MET’s computer science & IT graduate certificates share curriculum with MET’s online master’s degrees in computer information technology, which are ranked #12 in the nation by U.S. News & World Report.

Graduate with Applied AI & Machine Learning Expertise

Students who complete the Graduate Certificate in Applied AI & Machine Learning will be able to:

Solve complex problems such as computer vision, natural language processing, and speech recognition using machine learning algorithms, including supervised and unsupervised learning models, neural network architectures, and deep learning techniques.
Design and implement agents and algorithms for self-learning systems, leveraging AI models for data representation and prediction, implementing evolutionary and genetic algorithms for optimization, and developing software systems that incorporate AI models to enhance capabilities.
Evaluate the ethical implications of AI systems, ensuring model fairness, accountability, and transparency, and effectively communicating technical AI concepts to non-technical stakeholders.

Advance Your Career

BU MET’s Graduate Certificate in Applied AI & Machine Learning prepares you for a wealth of different roles, such as machine learning engineer, AI developer, data scientist, computer vision expert, ethical AI analyst, natural language processing (NLP) specialist, and AI product manager.

Computer Science Faculty

Guanglan Zhang

Associate Professor, Computer Science

Coordinator, Health Informatics Programs

Chair, Computer Science

Shengzhi Zhang

Associate Professor

Associate Chair, Computer Science

Coordinator, Web Application Development

Reza Rawassizadeh

Associate Professor, Computer Science

Farshid Alizadeh Shabdiz

Professor of the Practice, Computer Science

View All Faculty

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Online Applied AI & Machine Learning Graduate Certificate

At a Glance

Build Practical, Dynamic Artificial Intelligence Skills to Advance Your Career Across Industries