Graduate Certificate in Artificial Intelligence

Course 1: CSCA 5622: Introduction to Machine Learning: Supervised Learning
- 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](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5622)

Course 2: CSCA 5632: Unsupervised Algorithms in Machine Learning
- 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. [Learn more about the course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5632)

Course 3: CSCA 5642: Machine Learning Specialization - Introduction to Deep Learning
- 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. [Learn more about the course ](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5642)

Course 4: CSCA 5214: Computing, Ethics, and Society Foundations
- 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. [Learn more about the course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5214)

Course 5: CSCA 5224: Ethical Issues in AI and Professional Ethics
- 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](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5224)

Course 6: CSCA 5204 Current Issues in Ethics and AI
- The first module of this course introduces you to the topic of current ethical issues in AI from several perspectives. After providing an overview of the course including the learning objectives and what work is required of you, you’ll gain insights into key ethical theories that will underlie discussions throughout the course. These include Kantianism, Virtue Ethics, Utilitarianism, and Social Contract Theory. Then we’ll further motivate the course by looking at key ethical concerns that have been identified related to the uses of AI, and the range and pace of AI development and use. [Learn more about this course](https://www.coursera.org/learn/current-issues-in-ethics-and-ai)

Course 7: CSCA 5234: Ethical Issues in Computing Applications
- 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](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5234)

Course 8: CSCA 5834: Modeling of Autonomous Systems
- 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. [Learn more about this course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5834)

Course 9: CSCA 5844: Requirement Specifications for Autonomous Systems
- 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. [Learn more about this course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5844)

Course 10: CSCA 5854: Verification and Synthesis of Autonomous Systems
- 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. [Learn more about this course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5854)

Course 11: CSCA 5312: Basic Robotic Behaviors and Odometry
- "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! [Learn more about this course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5312)

Course 12: CSCA 5332: Robotic Mapping and Trajectory Generation
- 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](https://www.coursera.org/learn/basic-robotic-behaviors-and-odometry)”, before beginning this one. [Learn more about this course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5332)

Course 13: CSCA 5342: Robotic Path Planning and Task Execution
- ​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](https://www.coursera.org/learn/basic-robotic-behaviors-and-odometry)” and "[Robotic Mapping and Trajectory Generation](https://www.coursera.org/learn/robotic-mapping-trajectory-generation)" , before beginning this one. [ Learn more about this course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5342)

Course 14: CSCA 5112: Introduction to Generative AI
- 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. [Learn more about this course](https://www.colorado.edu/cs/academics/online-programs/mscs-coursera/csca5112)

Course 15: CSCA 5122: Modern Applications of Generative AI
- (In development)

Course 16: CSCA 5132: Advances in Generative AI
- (In development)

Course 17: CSCA 5832: Fundamentals of Natural Language Processing
- (In development)

Course 18: CSCA 5842: Deep Learning for Natural Language Processing
- (In development)

Course 19: CSCA 5852: Model and Error Analysis for Natural Language Processing
- (In development)