Welcome to the self paced course, Algorithms: Design and Analysis, Part 2! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience.

This course covers the theory of automata and languages. We begin with a study of finite automata and the languages they can define (the so-called "regular languages." Topics include deterministic and nondeterministic automata, regular expressions, and the equivalence of these language-defining mechanisms.

The course is based on the text Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who by coincidence are also the instructors for the course.

AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.

Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics.

The term “Computation” refers to the action performed by a computer. A computation can be a basic operation and it can also be a sophisticated computer simulation requiring a large amount of data and substantial resources. This course aims at introducing learners with no prior knowledge to the basic key concepts of computer science. By following the lectures and exercises of this course, you will gain an understanding of algorithms by programming using the language Ruby.

Software-based and physical side-channel attacks have similar techniques. But physical attacks can observe properties and side effects that are usually not visible on the software layer. Thus, they are often considered the most dangerous side-channel attacks. In this course, we learn both about physical side-channel attacks but also about more advanced software-based side channels using prefetching and branch prediction. You will work with these attacks and understand how to mitigate them.

You will learn how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory.

Learn how to design more complex programs, using new data structures, abstraction, and generative recursion.

Learn to enhance your code by using fundamental data structures and powerful algorithms in Java.

This course focuses on the mathematics, programming, operations, and algorithms of quantum computing

This course explores the basic design principles of today's quantum computer systems. In this course, students will learn to work with the IBM Qiskit software tools to write simple quantum programs and execute them on cloud-accessible quantum hardware.