Discover how artificial intelligence (AI) and machine learning are revolutionizing how society operates and learn how to incorporate them into your business—today.
The University of Chicago’s eight-week Artificial Intelligence and Machine Learning course guides participants through the mathematical and theoretical background necessary to take advantage of the machine learning at use in today’s business world.
Designed for those who already have previous knowledge working with Python and have a solid understanding of linear algebra.
Learn how to make full use of AI to benefit your customers. Throughout this course, you will discover how foundational data science models are leveraged to obtain increased technological power: more computing power, more complex layers, and different sampling techniques that will refine the accuracy of predictions.
After completing the course you will be able to:
- Interpret big data-related solutions
- Understand the basic concepts of predictive analytics and machine learning
- Use the scripting programming languages including Python to process, visualize, and analyze large data sets and implement machine learning solutions
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
- Eight weeks in length
- Weekly, self-paced interactive learning modules and assignments are time-sensitive and should be completed by the set deadlines
- Synchronous sessions and live question and answer sessions
- Mentors will provide continuous support and encourage a dynamic and positive learning environment
Weekly Course Schedule
Intro to Predictive Analytics and Evolution of ML
Familiarize yourself with data 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.
Data Exploration and Pattern Detection via Partitioning
Examine how to find structure in data, including clusters, density, and patterns. Discover why clustering analysis is useful and learn the mathematical background for distance metrics and its importance in machine learning, DBSCAN, HDBSCAN, optics, and expectation-maximization.
Data Transformation and Dimensional Reduction
Acquire knowledge 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, and topic models, as well as unstructured data concepts with the fundamentals of natural language processing. Delve into high-dimensional data visualization with UMAP and t-SNE and non-paramedic data soothing with kernel density estimation.
Supervised and Semi-Supervised Learning
Explore concepts like basic notation, training, and model development, the theory of loss function, and learning as an optimization algorithm. Learn about gradient descent-bayesian networks and ridge and Lasso, as well as descriptive classifiers like k-nearest neighbor and Naïve Bayes algorithm. Examine binary class learning, logistic regression, and Hinge, Jacobian, Hessian, and logarithmic loss.
Prediction with Support Vectors and Decision Trees
Discover the use of discriminative classifiers vs. descriptive classifiers, kernel trick and support vector machines, and tree-based prediction algorithmsLearn how to extract classification and regression rules from decision trees.
Understand voting classifiers, Bootstrap aggregation (bagging), boosting methods, and the random forest theoretical approach.
Learn about the essentials of model interpretation and regularization concept in machine learning, performance evaluation metrics, and cross-validation, dealing with imbalanced data, anomaly detection realm, and generalization error (overfitting vs. underfitting). Examine autoencoders with an introduction to neural networks.
Recommendation Systems, Graph Networks, and Social Network Analysis
Explore collaborative filtering, apriori algorithm, recommendation systems, and homogeneous vs. heterogeneous networks. Discover graph theory via social network analysis.
Meet Your Instructor
A multitude of large corporations, including Accenture, Amazon, IBM, and Microsoft, are using AI, applying large-scale machine learning to boost innovation. Career opportunities for professionals dedicated to AI and machine learning have grown to include the energy, farming, finance, manufacturing, and transportation industries. According to the World Economic Forum, AI/ML roles are the most in-demand in today’s job market.
Potential AI and Machine Learning Roles
- AI Engineer
- AI Specialist
- Business Intelligence Developer
- Data-Mining Analyst
- Data Scientist
- Machine Learning Engineer
- Machine Learning Researcher
- Machine Learning Specialist