Carnegie Mellon Machine Learning Major: Curriculum and Opportunities
Uncertainty pervades our lives, arising from various sources such as randomness, measurement errors, deception, and incomplete information. Statistics provides a framework for making predictions and decisions in the face of such uncertainty. It plays a crucial role in various fields, including public policy, law, medicine, industry, computing, technology, finance, and science. Statisticians possess diverse skills in computing, mathematics, decision-making, experimental design, forecasting, and interpreting analysis results. They also collaborate with experts in other fields to broaden their knowledge and apply statistical tools effectively.
The Department of Statistics & Data Science at Carnegie Mellon University (CMU) is renowned for its contributions to statistical theory and practice. Its faculty members are recognized globally for their expertise and have received prestigious awards. The department is dedicated to undergraduate education, with faculty members teaching courses at all levels. It also fosters a friendly and energetic environment with exceptional computing resources.
Bachelor of Science in Statistics and Data Science
The Bachelor of Science in Statistics and Data Science in the Dietrich College of Humanities and Social Sciences (DC) is a flexible program designed to equip students with a strong foundation in both the theory and practice of statistics. Students in this program develop and master skills in computing, mathematics, statistical theory, and the interpretation and display of complex data. They also gain experience in applying statistical tools to real-world problems in other fields and learn the nuances of interdisciplinary collaboration.
Core Requirements
The B.S. in Statistics and Data Science program has several core requirements designed to provide a comprehensive understanding of the field. These include:
- Calculus: Students must demonstrate proficiency in calculus by completing specified courses or passing the Mathematical Sciences assessment tests during First-Year Orientation. Completing the calculus requirement during the freshman year is recommended.
- Data Analysis: Data analysis involves extracting insights from data using various techniques and displays. Students gain hands-on experience with data analysis through introductory courses that cover similar topics but emphasize different examples. Advanced courses build upon this foundation by introducing more sophisticated methods.
- Probability and Statistical Theory: Probability theory provides a mathematical description of randomness and serves as the language for statistical models. Statistical theory offers a framework for making inferences about unknown quantities from data. Students who complete 36-235 are expected to take 36-236 to complete their theory requirements, while those who choose 36-225 will be required to take 36-226 afterward.
- Statistical Computing: Proficiency in coding data processing and analysis tasks is essential. Students are exposed to the programming language R throughout the curriculum, and they must also gain basic competency in Python by taking either Principles of Computing or Fundamentals of Programming and Computer Science.
- Advanced Statistics Courses: The Department of Statistics & Data Science offers advanced courses that focus on specific statistical applications or advanced statistical methods.
- Statistical Modeling: The implementation and interpretation of statistical models are central to the practice of statistics. Students learn to think critically about data, research goals, and the validity of models.
- Concentration Area: Students must complete a concentration area consisting of four related courses outside of Statistics & Data Science. This prepares them to deal with statistical aspects of problems in another field. The courses are usually drawn from a single discipline of interest and must be approved by the Statistics Undergraduate Director. The concentration area can be fulfilled with a minor or additional major, but not all minors and additional majors fulfill this requirement.
Mathematical Sciences Track
Students in the Bachelor of Science in Statistics and Data Science (Mathematical Sciences Track) program develop and master a wide array of skills in computing, mathematics, statistical theory, and the interpretation and display of complex data. In addition, Statistics majors gain experience in applying statistical tools to real problems in other fields and learn the nuances of interdisciplinary collaboration.
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Bachelor of Science in Statistics and Machine Learning
The Bachelor of Science in Statistics and Machine Learning is jointly administered by the Department of Statistics & Data Science and the Department of Machine Learning. Students in this major take courses focused on skills in computing, mathematics, statistical theory, and the interpretation and display of complex data.
Machine Learning: Fundamentals and Algorithms Program
CMU's School of Computer Science Executive Education offers a Machine Learning: Fundamentals and Algorithms program. This program aims to equip participants with the skills and knowledge to create functional tools for predicting unseen data and implement learning algorithms for classification, regression, and clustering. It is designed for engineers, data analytics professionals, and technical managers who want to integrate machine learning and AI into their work.
Curriculum
The program curriculum comprises 10 modules covering Python programming skills for machine learning applications. Participants learn to use decision trees, k-NN algorithm, and other fundamental algorithms. They also learn to refine algorithms, adapt them for regression, and use gradient descent to implement linear regression. The program covers topics such as logistic regression, overfitting, neural networks, and the k-means algorithm.
Program Experience
The program includes Python coding exercises in each module, bite-sized learning, knowledge checks, a dedicated program support team, a mobile learning app, and peer discussion. Participants receive a verified digital certificate of completion from Carnegie Mellon University School of Computer Science Executive Education upon successful completion of the program.
Prerequisites
Participants are expected to have prior experience with coding and a working understanding of linear algebra, calculus, probability, and statistics. A self-assessment is provided to help learners evaluate readiness for the program’s technical content before enrolling.
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Master of Science in Artificial Intelligence (MSAI)
The Master of Science in Artificial Intelligence (MSAI) program at Carnegie Mellon University is designed to prepare students to discover new AI applications and develop them into viable products. The curriculum consists of core courses, knowledge requirements, electives, and a capstone project.
Core Curriculum
The core curriculum is a five-course sequence based on the four main phases of innovation development: opportunity identification, opportunity development, business planning, and incubation of a business with a viable product. The courses must be taken in the order listed:
- Artificial Intelligence and Future Markets: This course surveys fields in which AI has been applied to identify areas ripe for AI development.
- Law of Computer Technology: This course reviews legal principles applicable to computer developments, including AI law and formation of startups.
- AI Engineering: This course focuses on integrating AI with legacy systems, covering supervised learning, feed-forward neural networks, and other topics.
- AI Innovation: This course involves projects sponsored by outside companies, where students work in teams to understand the business environment of the project and design a Minimum Viable Product (MVP).
- Capstone Project: The objective of the Capstone is for the team to develop a working product suitable for intrapreneurial integration into a company or suitable for startup investment.
Knowledge Requirements
The knowledge requirements consist of six rigorous courses to ensure that students are able to develop advanced AI applications:
- Coding Bootcamp
- Machine Learning
- Deep Learning
- Generative AI OR Large Language Models
- Natural Language Processing
- A 12-unit course in AI, NLP, or ML.
Internship
Every student is required to complete an industry internship during the summer between the first spring and second fall semesters.
Electives
Students must take at least three 12-unit elective courses or equivalent.
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Machine Learning Minor
The Machine Learning Minor allows students to learn about the core principles of this field. The minor requires five courses (two core courses and three electives) from the School of Computer Science (SCS) and the Department of Statistics and Data Science.
Prerequisites
The prerequisites for the Machine Learning Minor include:
- CS background: Introduction to Data Structures or Principles of Imperative Computation.
- Math background: Mathematical Foundations for Computer Science, Concepts of Mathematics, or Mathematical Concepts and Proofs.
- Probability & statistics background: Probability Theory for Computer Scientists, Probability Theory and Random Processes, Introduction to Probability Theory, Probability and Statistical Inference I, Probability and Computing, or Probability.
Core Courses
Students must take two core courses:
- Introduction to Machine Learning or Artificial Intelligence and Machine Learning I
- One of the following courses: Deep Reinforcement Learning and Control, Machine Learning With Large Datasets, Deep Learning Systems - Algorithms and Implementation, Intermediate Deep Learning, Machine Learning for Structured Data, Foundations of Learning, Game Theory and Their Connections.
Electives
Students need to take three elective courses from a list of approved courses. Students may substitute one of these courses with one semester of an SCS Senior Honors Thesis or equivalent senior research credit.
Undergraduate Concentration in Machine Learning
The Concentration in Machine Learning allows undergraduates to learn about the core principles of this field. The concentration requires five courses (two core courses and three electives) from the School of Computer Science (SCS) and the Department of Statistics and Data Science.
Prerequisites
The prerequisites for the Machine Learning Concentration include:
- CS background: Principles of Imperative Computation.
- Math background: Mathematical Foundations for Computer Science; Concepts of Mathematics; or Mathematical Concepts and Proofs.
- Probability & statistics background: Probability Theory for Computer Scientists; Probability Theory and Random Processes; Introduction to Probability Theory; Probability and Statistical Inference I; Probability and Computing; or Probability.
Core Courses
Students must take two core courses:
- Introduction to Machine Learning, or Artificial Intelligence and Machine Learning I
- One of the following courses: Deep Reinforcement Learning and Control, Machine Learning With Large Datasets, Deep Learning Systems - Algorithms and Implementation, Intermediate Deep Learning, Machine Learning for Structured Data, Foundations of Learning, Game Theory and Their Connections
Electives
Students need to take three courses from the following list, each being at least nine units. Students may substitute one of these courses with one semester of an SCS Senior Honors Thesis or equivalent senior research credit.
Master of Science in Machine Learning (MSML)
The Master of Science in Machine Learning (MSML) program at Carnegie Mellon University provides students with a comprehensive education in the theory and practice of machine learning. The curriculum consists of core courses and electives.
Core Courses
M.S. students take all six core courses:
- Introduction to Machine Learning or Advanced Introduction to Machine Learning
- Intermediate Deep Learning or Deep Reinforcement Learning or Advanced Deep Learning
- Probabilistic Graphical Models
- Machine Learning in Practice
- Optimization for Machine Learning (formerly Convex Optimization)
- Probability and Mathematical Statistics or Intermediate Statistics
Note: The core courses must be taken from separate lines.
Electives
Students take their choice of three elective courses (from separate lines).
Research Opportunities
The Statistics & Data Science program encourages students to gain research experience through opportunities such as Summer Undergraduate Research Apprenticeships (SURA), departmental capstone courses, and independent study. The faculty in the department largely work within the domains of statistical theory and methodological development, areas that require advanced mathematical training.
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