Fundamentals of Applied Artificial Intelligence Certificate Courses

This course covers the mathematical principals required for the various concepts in the area of applied artificial intelligence. This course aims at delivering the mathematical topics in a balance manner based on solid theoretical foundation while focusing on the computational aspects and application to data problem. Topics covered include linear algebra, multivariate calculus, optimization, regression, statistics of datasets, orthogonal projections, principal component analysis, and probability. The course provides computational and practical examples of the covered topics.

This course covers programming techniques used in AI applications. Topics include programming constructs, I/O, conditional constructs, iterative control structures, structured decomposition, method call and parameter passing, classes, 1-D and 2-D arrays, libraries, APIs, and Data Structures. The course will use Python with several tools where students learn programming with a beginner-friendly introduction to Python and AI libraries including learning how to analyze data, integrate and use basic machine learning algorithms and APIs, create visualizations, implement and test some models, and analyze results.

This course covers the essential machine learning techniques and algorithms and their applications. Topics include supervised and unsupervised learning, clustering, classification algorithms, linear regression, support vector machines, decision trees, random forests, neural network, deep learning, and reinforcement learning. Throughout the course, students will be exposed to real-world industrial, business, medical and social problems, where the obtained skills are employed to handle data and develop machine learning based solutions. The material and structure of the course are designed with a preference for the practical knowledge of AI more than mathematical or theoretical concepts. Different Machine Learning applications will be discussed including computer vision, natural language processing, time-series prediction, speech recognition, sentiment analysis, cybersecurity, among others.

This course covers principles of deep learning and in its applications. Students will learn how to build and use different kinds of deep neural networks using hands-on approach. Topics include: feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers and encoders/decoders. The course will include hands-on applications covering natural language processing tasks, behavioral analysis, financial analysis and anomalies detection.