Online AI CurriculumOnline AI CurriculumOnline AI Curriculum

Course goals: The goal of this bridge course is to ensure that participants meet the prerequisites for other courses in this program. The material in this course serves as a review and refresher of basic concepts in programming, data structures, and math (discrete math, calculus, linear algebra, and probability theory).

Exams and assignments: Five (5) pretests and five (5) posttests in Python, data structures, calculus, linear algebra, and probability. This course is ungraded; however, we expect learners to complete all posttests. If you score lower than 65% on any posttest, we strongly encourage you to review the segment and retake the assessment.

Topics covered: programming in Python, data structures, discrete math, calculus, linear algebra, and probability.

Course goals: Learn AI fundamentals and key ideas behind the design of intelligent agents for real-world problems, including search, games, machine learning, and constraint satisfaction. Gain exposure to applications of AI and machine learning in business—such as customer service, sales, and marketing—and study how AI is used in other industries like retail, finance, health care, and manufacturing.

Exams and assignments: Learners will complete eight (8) quizzes (50%) and three (3) assignments (50%).

Topics covered: history and evolution of AI, search, constraint satisfaction problems, adversarial search, machine learning, logical agents, and AI ethics.

Course goals: In the first half of the course, you will learn how to design and analyze algorithms, with the goal of developing an algorithmic mindset: how to think from specification of a problem to designing an efficient algorithm for it. In the second half of the course, you will focus on supervised learning techniques for regression and classification on real-world data sets.

Exams and assignments: Learners will complete four (4) homework assignments, each worth 25% of the total course grade. The passing grade for this course is 70% or higher.

Topics covered: performance of algorithms; design techniques for optimization problems; graph algorithms; linear programming and hardness; overview of machine learning; statistical models for prediction; linear models; and inductive biases, optimization, and objectives.

Course goals: Learn about the theoretical and programming aspects of neural networks (ANNs) and deep learning (DL) models. This course explores the invention, history, and development of ANNs and DL models; describes their relationship to machine learning; and identifies the ways they can be used to solve a variety of industry and business problems.

Exams and assignments: Learners will complete five (5) assignments (50% of course grade); eight (8) quizzes (20% of course grade); and one (1) final exam (30% of course grade).

Topics covered: neural networks; deep learning; machine learning; deep forward networks; learning and back propagation; convolutional neural networks; optimization, regularization, and practical methodology; object detection; recurrent neural networks; auto-encoders, generative models; and generative adversarial networks.

Course goals: This course provides an introduction to the field of natural language processing (NLP). Learners will learn how to create systems that can analyze, understand, and produce language. In addition to machine-learning methods for NLP, this course covers a range of applications (e.g., information extraction, machine translation, text generation, and automatic summarization) and the role of linguistic concepts.

Exams and assignments: Learners will complete three (3) assignments (75%). Class participation accounts for 25% of the course grade. The passing grade for the course is 65% or higher.

Topics covered: language modeling; text classification, sentiment, and neural nets; word embeddings and sequence modeling; semantics and machine translation; extractive summarization; abstractive summarization and language generation; information extraction and dialogue; and bias and industrial applications.

Course goals: Understand the fundamentals and the applications of robotics and computer vision. This course will cover the underlying theory and principles of the sensorimotor loop, and translate those learnings into practice. Learners who successfully complete this course will gain an understanding of how many of the state-of-the-art robots and computer vision systems work.

Exams and assignments: Learners will complete four (4) homework assignments (50%) and four (4) quizzes (50%). The passing grade for this course is 65% or higher.

Topics covered: robotics, motion planning, robot simulation, computer vision, object recognition, geometry, video, and robot learning.

Course goals: Gain an understanding of the security, privacy, and policy aspects surrounding machine learning systems. In addition to the legal requirements for privacy and security in different jurisdictions, this course addresses the security and privacy attacks that can affect machine learning models, and advanced privacy technologies that can be used to increase data protection and personal privacy.

Exams and assignments: Learners will complete six (6) exercises worth a collective 72% of the course grade. Class participation accounts for 28% of the course grade. The passing grade for the course is 65% or higher.

Topics covered: law and policy frameworks (e.g., GDPR, HIPAA); security and privacy; advanced privacy technologies; machine learning attacks and defenses; and differential privacy.