Most affordable Masters program in Artificial Intelligence and Machine Learning

The Electrical and Computer Engineering Department (ECE) offers the Electrical and Computer Engineering M.S.-Machine Learning and Signal Processing (MLSP) named option program which is intended for students looking for an advanced entry into a data science career in industry.

Students will learn quantitative thinking, practical problem-solving, computer programming, and applications to a variety of domains. It is designed to deepen the student's technical knowledge and sharpen their professional skills for a well-prepared entry into industry. The program provides a practical focus through a course-only curriculum, an accelerated and predictable 16-month completion time, and a professional development hands-on project requirement. Well-prepared students and UW–Madison undergraduates may find it feasible to complete the program in 12 months.

The required coursework draws upon foundational and cutting-edge methods in MLSP, and is taught by faculty conducting pioneering research in the field. Successful students will have some experience with linear algebra, statistics, and programming. The combined focus on the mathematical foundations of data science and their practical application to real-world problems will prepare graduates to be ready to immediately contribute in a variety of different jobs across data science, machine learning, and signal processing.

The focus of the MLSP program differs from the traditional research-based M.S. program. MLSP students do not conduct independent research and prepare a thesis, but rather have an accelerated course plan focused in the MLSP area with a professional development hands-on project, either via an internship/co-op or an independent project. Students also have the opportunity to take select courses from Engineering Professional Development. If you are interested in research and advanced concept development, you are better served pursuing a research-based M.S. program or a Ph.D. program. If you want to complete your degree in 12–16 months and have a fast-track into the data science workforce, then the MLSP program is right for you.

For a Master of Science in Computer Science, Specialization in Machine Learning (15 hours), students must select from the following:.

The following is a complete look at the courses that may be selected to fulfill the Machine Learning specialization, regardless of campus only courses listed with bold titles are offered through the online program.

CSE 6740 Computational Data Analysis: Learning, Mining, and Computation.

Elective ML courses must have at least 1 3 of their graded content based on Machine Learning.

CS 7642 Reinforcement Learning and Decision Making(Formerly CS 8803-O03).

You can find the list of current OMSCS courses here.

The MS in Statistics & Machine Learning prepares you for a range of career possibilities in statistics and machine learning and helps you stand out in the marketplace.

The need for professionals trained in the mathematical techniques of machine learning is greater than ever. In this program, you’ll work closely with faculty who have extensive backgrounds in applied mathematics, data science/analytics, computational science, statistics, industrial modeling, and much more. In lieu of one formal course, students may conduct a semester’s worth of independent study with a research advisor that leads to publication of a quality technical report in an area of statistical/data sciences.

This program is STEM designated, allowing international students who hold F-1 visas to apply for OPT work authorizations for a total of 36 months (an initial 1-year period and a 24-month OPT STEM extension) of paid work experience in the U.S. after graduation.

The Master of Science in Artificial Intelligence and Machine Learning (AIML) provides a strong foundation in the artificial intelligence and machine learning fields with foci on mathematical foundations, algorithms, tools, and applications as they pertain to artificial intelligence and machine learning. Students will gain competency in fundamental methods and techniques in artificial intelligence and machine learning. Their understanding will be applied to real data sets and data analysis tasks with the help of state-of-the-art technologies, tools, and platforms. The Master of Science in Artificial Intelligence and Machine Learning program culminates in a two-term capstone experience where students work on a real-world or research problem using the knowledge they have gained throughout the program.

The machine learning master’s program establishes the theoretical and practical foundations necessary to be at the forefront of progress in the next technological revolution. Advancements made in machine learning and related disciplines will soon touch every piece of technology, making an advanced degree an essential asset for a successful career.

The rapidly expanding field of machine learning has a multitude of applications in diverse areas such as intelligent systems, computer vision, speech recognition, natural language processing, robotics, finance, information retrieval, bioinformatics, healthcare, and weather prediction.

The unique machine learning master’s program develops a thorough understanding of deep learning theory and familiarizes you with the most important paradigms. This allows you to apply or develop the appropriate methods in real-world applications, whether pursuing a career in industry, academia, or research.

The machine learning master's program is available fully online and is a Top 10 Online Graduate Computer Information Technology program by US News and World Report.

The MS in Electrical and Computer Engineering – Concentration in Artificial Intelligence (AI) is open to students who want to pursue a career in artificial intelligence and professionals who want to further their knowledge and skills. Participants learn the fundamentals of artificial intelligence, machine learning, computer vision, and neural networks with emphasis using hands-on instruction resulting in a final capstone project.

Artificial Intelligence and Machine Learning are important areas of study in Robotics that center on the problems of enabling robots to make coherent decisions based on available data.

In addition to fulfilling the requirements of the robotics masters, the students may elect to take the following courses. Note that this suggested set of courses covers 2 of the 3 required areas so students would only need to take one other course in either the Control or Robot Design category to complete the foundational requirements and achieve the desired level of breadth.

The Master of Science in Electrical and Computer Engineering - Machine Learning and Data Science program offers students a focused, demanding education in the theories, approaches, and applications of data science, machine learning, signal processing, and information processing.

The Computer Science Master's Degree - Machine Learning Track advance the knowledge of machine learning methods and applications might consider the Computer Science Master's Degree - Machine Learning Track. Machine learning is a fast developing field with several applications in a wide range of fields, including bioinformatics, fraud detection, intelligent systems, perception, finance, information retrieval, and other fields.

The Master of Science in Artificial Intelligence (MSAI) provides a comprehensive framework of theory and practice in the emerging field of AI. The program delivers the foundational knowledge needed to explore both key contextual areas and complex technical applications of AI systems.

Curriculum incorporates elements of data science, robotics, and machine learning enabling you to pursue a holistic and interdisciplinary course of study while preparing for a position in AI research, operations, software or hardware development, or doctoral degree in a sector poised for explosive growth.

The MS in Artificial Intelligence program features five core courses Programming Design Paradigms, Algorithms, Foundations of AI, Machine Learning, and Intelligent Interaction that represent the essential knowledge for research and development in Artificial Intelligence. These courses examine the foundational representations and processes used across AI systems, how machine learning can be used to acquire this knowledge and the various ways AI systems interact with people, as well as an understanding of the ethical concerns in developing AI technologies responsibly. The courses are tailored toward technically or mathematically trained students.