Artificial Intelligence and Machine Learning
Drive tangible value and shape the future of your organization.
Enroll Now Customize for Organizations
At a Glance
- Enrollment:
- Open enrollment
- Length:
- Eight weeks
- Format:
- Online
Upcoming Dates
July Start
Discover how artificial intelligence and machine learning are revolutionizing society and incorporate the technology into your business.
The University of Chicago’s eight-week Artificial Intelligence and Machine Learning course guides participants through the mathematical and theoretical frameworks necessary to leverage the machine learning in use today in the business world.
Designed For
Designed for professionals, including consultants and technical practitioners, with working knowledge of Python and a solid foundation in linear algebra looking to turn data into tangible results through artificial intelligence (AI) and machine learning (ML).
Learning Objectives to Become an AI and ML Expert
Artificial intelligence supports innovation by leveraging data and algorithms to mimic human learning. In this course, you will create machine-learning algorithms using Jupyter Notebook and Python to solve complex problems. You will also explore clustering, supervised learning, multi-attribute data, recommendation systems, graph networks, pattern anomaly detection, and more.
After completing the course you will be able to:
- Identify big data-related solutions.
- Define the basic concepts of predictive analytics and machine learning.
- Use scripting programming languages, including Python, to process, visualize, and analyze large data sets.
- Implement machine-learning solutions.
- Earn a credential certifying completion from the University of Chicago and become part of the UChicago network.
Ready to Take Your Career to the Next Level?
Register today and unite your professional practice with our distinctive blend of academic rigor and real-world application.
Enroll NowAI and Machine Learning Curriculum
You will learn to:
- Design classification and regression models for prediction and reasoning.
- Develop a comprehensive understanding of model interpretation and evaluation.
- Perform unsupervised and supervised machine learning on large-scale, unstructured/structured datasets.
- Understand important pattern discovery concepts, methods, and applications.
Methodologies and Techniques:
Gephi
GitHub
Jupyter Notebook
Python
AI and ML Online Format Features
- Self-paced interactive learning modules with a variety of engaging learning activities, assignments, and resources.
- Live sessions that bring you, your peers, and your instructor together to learn collaboratively about the current state of the field, engage with real-world problems, and explore authentic solutions.
- Continuous support from your instructional assistant, who will accompany you on your journey through the content, answer your questions, and provide feedback on your work.
Weekly Course Schedule
Familiarize yourself with predictive analytics, machine learning applications, and positioning in data-driven decision-making within the business world. Learn about types of decisions and tools like regression, classification, recommendation, and retrieval. Discover how to connect data, domain knowledge, business problems, and analytics, and learn to answer business questions with data visualization, nuances, and modalities. Explore neural networks, deep learning, and reinforcement learning concepts.
Examine data exploration and pattern detection via partitioning, focusing on unsupervised big data algorithms. Discover why clustering analysis is useful and learn the mathematical background for distance metrics and machine learning, DBSCAN, HDBSCAN, optics, and the expectation-maximization algorithm.
Learn about of feature selection, extraction, and transformation, singular value decomposition (SVD), independent component analysis, and truncated SVD. Continue with data transformation, projection, and dimension reduction to better understand SVD. Examine feature embeddings, text transformations, topic models, unstructured data concepts, and the fundamentals of natural language processing. Delve into high-dimensional data visualization through UMAP and t-SNE and non-paramedic data soothing with kernel density estimation.
Explore supervised and semi-supervised learning. Examine concepts like basic notation, training, model development, the theory of loss function, and learning as an optimization algorithm. Learn about gradient descent-bayesian networks, ridge and Lasso, and descriptive classifiers like the k-nearest neighbor and the Naïve Bayes algorithm. Examine binary class learning, logistic regression, and Hinge, Jacobian, Hessian, and logarithmic loss.
Explore the use of discriminative classifiers vs. descriptive classifiers, kernel trick and support vector machines, and tree-based prediction algorithms. Learn how to extract classification and regression rules from decision trees.
Focus on voting classifiers, bootstrap aggregation (bagging), boosting methods, and the random forest theoretical approach.
Learn the essentials of model interpretation and regularization in machine learning, performance evaluation metrics, cross-validation, imbalanced data handling, anomaly detection, and generalization error (overfitting vs. underfitting). Examine autoencoders with an introduction to neural networks.
Explore collaborative filtering, the apriori algorithm, recommendation systems, and homogeneous vs. heterogeneous networks. Discover graph theory via social network analysis.
Earn a Credential in AI and ML
After successful completion of this course, participants will receive credentials certified by the University of Chicago including a digital badge to recognize their achievement.
Meet Your Instructor
Our highly trained instructors are courageous thinkers and passionate leaders who leverage years of industry expertise and up-to-date knowledge of terminology, tools, and trends to deliver an unparalleled learning experience. Through their rigorous discourse, cross-disciplinary collaboration, and field-shaping contributions, they create practical solutions and pioneering innovations that enrich our world.
This instructor teaches this course regularly. Please speak to your enrollment advisor if you wish to know who the current teacher is.
Utku Pamuksuz, PhD
Associate Clinical Professor and Cofounder of Inference Analytics
Utku Pamuksuz is an AI and analytics instructor with expertise in data science, applied mathematics, and machine and deep learning. As a frequent guest speaker, he delivers academic and professional seminars. His published research involves AI algorithms in management, finance, strategy, healthcare...
Taras Skavinskyy, MSc
Director of Analytics at CBRE
Taras Skavinskyy leads a team of statisticians and data scientists in CBRE's Retail Analytics department, focusing on team development, user-friendly analytics software, and collaboration with leadership on future data products. He and his team harness advanced statistical methods, machine learning...
Career Outlook
A multitude of large corporations, including Accenture, Amazon, IBM, and Microsoft, are using AI and applying large-scale machine learning to boost innovation. Career opportunities for AI and ML professionals have expanded to include roles in the energy, farming, finance, manufacturing, and transportation industries.
The average annual base pay for a machine learning engineer in the United States.
The rank of AI and machine learning specialists among the most in-demand tech roles.
The projected CAGR for the global artificial intelligence market from 2022 to 2030.
Potential Job Titles for Professionals with AI and ML Expertise
- AI Engineer
- AI Specialist
- Business Intelligence Developer
- Data-Mining Analyst
- Data Scientist
- Machine Learning Engineer
- Machine Learning Researcher
- Machine Learning Specialist
How Do I Get Started?
-
Complete the form on the registration page.
-
Pay the tuition fee through our secure gateway.
-
Receive a welcome email with your login information for the virtual campus.
-
Gain access to the course content prior to the start date.
Of Interest
Statistics for Data Science
Learn to solve complex problems with data.