Graduate Certificate in Artificial Intelligence
Students in the Graduate Certificate in Artificial Intelligence program will build a strong foundation in key AI topics: Machine Learning (ML), ethical issues, control in the design of robotic and intelligent systems; and vital topics in generative AI reinforcement learning, natural language processing, and autonomous systems.
The deadline to enroll is October 4, 2024
This Graduate Certificate qualifies as credit toward your Master of Science in Computer Science degree.
Enroll by October 4, 2024
Classes start August, 26 2024
6–9 months
The certificate is 12 credits and can be completed in approximately in 6-9 months, depending on chosen course load per session
$525 per credit
$6,300 total cost
100% online
No application required
Gain a comprehensive foundation in Artificial Intelligence from this Graduate Certificate.
Students will be able to identify the ethical implications in the design and application of AI technology.
Contribute to the emerging discussion in these areas as ethical developers of new technologies.
Understand computer science foundations, probability/statistics, programming languages, and computer systems.
Students’ knowledge will extend with how ideas from these sub-disciplines of computer science support AI systems and vice versa.
Keep up with the state-of-the-art methods and techniques in this rapidly changing discipline of AI.
Students will read and comprehend research papers and consider how the research can be applied in their everyday practice.
Program description
Overview
The graduate certificate in Artificial Intelligence (AI) provides students with a strong foundation in key AI topics. Students apply Machine Learning (ML) algorithms to real-world data sets; examine ethical issues in the design and implementation of current and future computing systems and technologies; create an appreciation for the tight interplay between mechanism, sensor, and control in the design of robotic and intelligent systems; study vital topics in generative AI, reinforcement learning, natural language processing, and autonomous systems.
Credits earned in the AI Graduate Certificate can count toward the MS-CS degree and the AI Certificate. You can complete your AI Certificate in parallel with your MS-CS degree.
Required background
There are no formal prerequisites, but we recommend you have prior knowledge of basic mathematical concepts and computer programming.
- Math: Calculus, Discrete Mathematics, Probability, and Statistics and Linear Algebra
- Programming: Python and R Programming
If you still need to gain this knowledge, we encourage you to try non-credit coursework before attempting for-credit courses. If you would like to brush up on the above skills before starting the program, consider the following classes on Coursera:
- Calculus: Algebra and Differential Calculus
- Linear Algebra: Essential Linear Algebra
- R Programming: Mastering Software Development in R Specialization by Johns Hopkins University
- Python: Introduction to Scripting in Python Specialization by Rice University
Skills you will gain
- AI and Machine Learning theory
- Design and Implementation skills
- Ethical AI discernment
- Computer Science foundations
- State-of-art knowledge
- Real-world project experience
12 required courses (in 4 full specializations)
Course 1 of 18
CSCA 5622: Introduction to Machine Learning: Supervised Learning
Overview
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We’ll cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM.
Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. In this course, you will need to have a solid foundation in Python or sufficient previous experience coding with other programming languages to pick up Python quickly. We will be learning how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. The course is designed for programmers beginning to work with those libraries. Prior experience with those libraries would be helpful but not necessary.
College-level math skills, including Calculus and Linear Algebra, are required. Our hope for this course is that the math will be understandable but not intimidating. This course can be taken for academic credit as part of Learn more about the course
Course 2 of 18
CSCA 5632: Unsupervised Algorithms in Machine Learning
Overview
One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we’ll learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms.
Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. College-level math skills, including Calculus and Linear Algebra, are needed. It is recommended, but not required, to take the first course in the specialization, Introduction to Machine Learning: Supervised Learning.
Course 3 of 18
CSCA 5642: Machine Learning Specialization - Introduction to Deep Learning
Overview
Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course includes several hands-on projects, including cancer detection with CNNs, RNNs on disaster tweets, and generating dog images with GANs.
Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. We recommend taking the two previous courses in the specialization, Introduction to Machine Learning: Supervised Learning and Unsupervised Algorithms in Machine Learning, but they are not required. College-level math skills, including Calculus and Linear Algebra, are needed. Some parts of the class will be relatively math intensive.
Course 4 of 18
CSCA 5214: Computing, Ethics, and Society Foundations
Overview
Computing systems and technologies fundamentally impact the lives of most people in the world, including how we communicate, get information, socialize, and receive healthcare. This course is the first of a three-course sequence that examines ethical issues in the design and implementation of computing systems and technologies and reflects upon the broad implication of computing on our society. It covers ethical theories, privacy, security, social media, and misinformation.
Course 5 of 18
CSCA 5224: Ethical Issues in AI and Professional Ethics
Overview
Computing systems and technologies fundamentally impact the lives of most people in the world, including how we communicate, get information, socialize, and receive healthcare. This course is the second of a three course sequence that examines ethical issues in the design and implementation of computing systems and technologies, and reflects upon the broad implication of computing on our society. It covers algorithmic bias in machine learning methods, professional ethics, and issues in the tech workplace.
Learn more about this course
Course 6 of 18
CSCA 5234: Ethical Issues in Computing Applications
Overview
Computing systems and technologies fundamentally impact the lives of most people in the world, including how we communicate, get information, socialize, and receive healthcare. This course is the third of a three course sequence that examines ethical issues in the design and implementation of computing systems and technologies, and reflects upon the broad implication of computing on our society. It covers medical applications, uses of robotics, autonomous vehicles, and the future of work. Learn more about this course
Course 7 of 18
CSCA 5834: Modeling of Autonomous Systems
Overview
This course will explain the core structure in any autonomous system which includes sensors, actuators, and potentially communication networks. Then, it will cover different formal modeling frameworks used for autonomous systems including state-space representations (difference or differential equations), timed automata, hybrid automata, and in general transition systems. It will describe solutions and behaviors of systems and different interconnections between systems.
Course 8 of 18
CSCA 5844: Requirement Specifications for Autonomous Systems
Overview
This course will discuss different ways of formally modeling requirements of interest for autonomous systems. Examples of such requirements include stability, invariance, reachability, regular languages, omega-regular languages, and linear temporal logic properties. In addition, it will introduce non-deterministic finite and büchi automata for recognizing, respectively, regular languages and omega-regular languages.
Course 9 of 18
CSCA 5854: Verification and Synthesis of Autonomous Systems
Overview
This course will provide different techniques on the verification of autonomous systems against stability, regular, or omega-regular properties. Such techniques include Lyapunov theories, reachability analysis, barrier certificates, and model checking. Finally, it will introduce several techniques on designing controllers enforcing properties of interest over the original autonomous systems.
Course 10 of 18
CSCA 5312: Basic Robotic Behaviors and Odometry
Overview
"Basic Robotic Behaviors and Odometry" provides you with an introduction to autonomous mobile robots, including forward kinematics (“odometry”), basic sensors and actuators, and simple reactive behavior. This course is centered around exercises in the realistic, physics-based simulator, “Webots”, where you will experiment in a hands-on manner with simple reactive behaviors for collision avoidance and line following, state machines, and basic forward kinematics of non-holonomic systems. An overarching objective of this course is to understand the role of the physical system on algorithm design and its role as source of uncertainty that makes robots non-deterministic. If you are interested in getting started with robotics, this course is for you!
Course 11 of 18
CSCA 5332: Robotic Mapping and Trajectory Generation
Overview
In this second course of the Introduction to Robotics specialization, "Robotic Mapping and Trajectory Generation", you will learn how to perform basic inverse kinematics of (non-)holonomic systems using a feedback control approach. You will also learn how to process multi-dimensional sensor signals such as laser range scanners for mapping. Additionally, you will apply the overarching focus of mechanisms and sensors as sources of uncertainty and gain techniques to how to model and control them. It is recommended that you complete the first course of this specialization, “Introduction to Robotics: Basic Behaviors”, before beginning this one.
Course 12 of 18
CSCA 5342: Robotic Path Planning and Task Execution
Overview
This course, which is the last and final course in the Introduction to Robotics with Webots specialization, will teach you basic approaches for planning robot trajectories and sequence their task execution. In "Robotic Path Planning and Task Execution", you will develop standard algorithms such as Breadth-First Search, Dijkstra's, A* and Rapidly Exploring Random Trees through guided exercises. You will implement Behavior Trees for task sequencing and experiment with a mobile manipulation robot "Tiago Steel". It is recommended that you complete the first and second courses of this specialization, “Introduction to Robotics: Basic Behaviors” and "Robotic Mapping and Trajectory Generation" , before beginning this one. Learn more about this course
Course 13 of 18
CSCA 5112: Introduction to Generative AI
Overview
This introductory course offers a comprehensive exploration of Generative AI, including Transformers, ChatGPT for generating text, and Generative Adversarial Networks (GANs), the Diffusion Model for generating images. By the end of this course, you will gain a basic understanding of these Generative AI models, their underlying theories, and practical considerations. You will build a solid foundation and become ready to dive deeper into more advanced topics in the next course.
Course 14 of 18
CSCA 5122: Modern Applications of Generative AI
Overview
(In development)
Course 15 of 18
CSCA 5132: Advances in Generative AI
Overview
(In development)
Course 16 of 18
CSCA 5832: Fundamentals of Natural Language Processing
Overview
(In development)
Course 17 of 18
CSCA 5842: Deep Learning for Natural Language Processing
Overview
(In development)
Course 18 of 18
CSCA 5852: Model and Error Analysis for Natural Language Processing
Overview
(In development)
The certificate will be stackable, and the credits can be applied to the Master of Science in Computer Science (MS-CS) degree.
By pursuing the MS-CS on Coursera degree, students will take a broad approach to studying computer science that directly reflects a career in the field. Students explore coursework that represents fast-changing developments in AI and robotics, with opportunities to specialize in other job-relevant subjects through interdisciplinary electives in electrical engineering, engineering management, and data science. Admission to this fully accredited program is based on students’ performance in three preliminary courses, not their academic history.
Students will gain admission into the degree by completing a three-course pathway for credit with at least a B in each course —even if you do not hold a bachelor’s degree. No transcripts or applications are required! Because pathway courses count as part of the required curriculum, you make direct progress on your degree as you work toward admission.
Upon completion of the Artificial Intelligence Graduate Certificate, you can apply these 12 credits to the Master of Science in Computer Science degree. You’ll then have 40% of the degree completed.
Certificate
Graduate Certificate in Artificial Intelligence
Graduate Certificate in Artificial Intelligence Certificate Earn credit directly towards the:
Instructors
Frequently asked questions
Coursera does not grant academic credit; the decision to grant, accept, or recognize academic credit, and the process for awarding such credit, is at the sole discretion of the academic institutions offering the Graduate Certificate program and/or other institutions that have determined that completion of the program may be worthy of academic credit. Completion of a Graduate Certificate program does not guarantee admission into the full Master’s program referenced herein, or any other degree program.