Bachelor of Science in Artificial Intelligence and Decision Making

4 Years On Campus Bachelors Program

Massachusetts Institute of Technology

Program Overview

MIT’s Bachelor of Science in Artificial Intelligence and Decision Making (Course 6-4) integrates computer science, artificial intelligence, and decision science into a cohesive curriculum. The program emphasizes the development of intelligent systems that can learn, reason, and make decisions autonomously, preparing students for roles in AI research, development, and application.

Core Curriculum Components

Mathematical & Theoretical Foundations

  • 6.1200[J]: Mathematics for Computer Science

  • 6.3700: Introduction to Probability

  • 18.06: Linear Algebra

  • 18.03: Differential Equations

Artificial Intelligence Core

  • 6.3900 (Introduction to Machine Learning): Core principles of supervised and unsupervised learning.

  • 6.036 (Introduction to Machine Learning): Fundamental algorithms in ML.

  • 6.4010 (Artificial Intelligence): Foundations of AI methods and applications.

  • 6.4210 (Robotics): Control, perception, and design of robotic systems.

Decision Making & Optimization

  • 6.2550 (Optimization Methods): Optimization techniques in machine learning and decision systems.

  • 6.2620 (Decision Analysis): Structuring and analyzing decisions under uncertainty.

  • 6.3410 (Dynamic Systems & Control): Modeling and controlling dynamic systems.

Programming & Systems

  • 6.100A (Introduction to Programming in Python)

  • 6.1210 (Introduction to Algorithms)

  • 6.1020 (Software Construction)

Restricted Electives

Students choose from advanced electives in:

  • Deep Learning

  • Computer Vision

  • Natural Language Processing

  • Reinforcement Learning

  • Computational Cognitive Science

Communication-Intensive Subject (CI-M)

Examples include:

  • 6.UAT: Oral Communication in EECS

  • 6.811: Principles and Practice of Assistive Technology (CI-M)

Experiential Learning (Research, Projects, Internships etc.)

MIT’s AI & Decision Making program prioritizes experiential, hands-on learning that integrates theory with real-world applications:

Undergraduate Research Opportunities Program (UROP)

Students actively engage in AI research under MIT faculty mentorship, exploring areas such as deep learning, autonomous systems, AI ethics, and human-AI interaction. UROP fosters early research exposure and collaboration on groundbreaking AI projects.

Capstone & Project-Based Learning

The curriculum includes project-based courses and optional senior capstone projects where students design and implement AI solutions addressing complex, real-world challenges.

Interdisciplinary Collaboration

Students work across fields like biology, neuroscience, economics, and mechanical engineering—applying AI to diverse domains such as healthcare, climate modeling, and robotics.

Hackathons & Competitions

Students frequently participate in:

  • MIT AI & Robotics Hackathons

  • International Data Science Competitions

  • MIT AI Ethics Challenges
    These foster creativity, teamwork, and rapid prototyping under real-world constraints.

Industry Internships & Partnerships

MIT’s extensive industry connections facilitate internships at top AI companies (e.g., Google DeepMind, OpenAI, Tesla AI), research labs, and startups, offering practical experience in deploying AI at scale.

Progression & Future Opportunities

Graduates of MIT’s Artificial Intelligence and Decision Making program emerge as versatile AI professionals and researchers, highly sought after in academia, industry, and government.

Career Prospects Include:

  • AI Research Scientist at leading labs (e.g., Google Brain, Meta AI, OpenAI).

  • Machine Learning Engineer at top tech firms and startups.

  • Robotics Engineer in autonomous vehicles or robotics companies.

  • Quantitative AI Specialist in fintech, hedge funds, and investment firms.

  • AI Consultant or Strategist guiding enterprise AI transformation.

  • AI Policy Advisor in think tanks or government roles.

Many graduates pursue advanced degrees (MS, PhD) in AI, computer science, robotics, data science, or computational neuroscience at elite global institutions.

Additionally, the program cultivates transferable skills—logical reasoning, ethical awareness, interdisciplinary problem-solving—preparing graduates to lead in emerging fields such as AI ethics, explainable AI, human-centered AI, and AI entrepreneurship.

Overall, MIT’s AI & Decision Making graduates are empowered to drive innovation at the forefront of AI, bridging technical expertise with impactful, ethical decision-making in a rapidly evolving technological world.

Program Key Stats

$61,990
$ 75
Aug Intake : 6th Jan


8 %
No
Yes

Eligibility Criteria

AAA - A*A*A
3.5 - 4.0
38 - 42
90 - 95

1510 - 1580
34 - 36
7.0
90

Additional Information & Requirements

Career Options

  • Graduates of MIT’s Bachelor of Science in Artificial Intelligence and Decision Making (Course 6-4) are uniquely trained at the intersection of artificial intelligence
  • machine learning
  • and decision theory
  • This interdisciplinary foundation equips them for cutting-edge roles in AI-driven industries
  • enabling them to design
  • implement
  • and optimize intelligent systems across diverse sectors
  • Technology & AI Engineering AI Engineer: Building scalable AI systems and architectures
  • Machine Learning Engineer: Developing machine learning pipelines and algorithms
  • Natural Language Processing (NLP) Engineer: Designing systems for text and speech understanding
  • Computer Vision Engineer: Creating solutions for image and video analysis
  • Robotics Engineer: Developing autonomous systems and intelligent robotics
  • Data Science & Analytics Data Scientist: Transforming large datasets into actionable insights
  • Decision Scientist: Applying AI to optimize decision-making processes
  • Predictive Modeler: Building forecasting models for dynamic systems
  • AI Research Scientist: Conducting foundational research in AI and ML methods
  • Business & Consulting AI Strategy Consultant: Advising organizations on AI integration
  • Business Intelligence Analyst: Leveraging AI for operational improvements
  • AI Product Manager: Leading development of AI-powered products
  • Digital Transformation Consultant: Driving AI adoption in enterprise settings
  • Finance & Quantitative Roles Quantitative Researcher (AI Focus): Building AI models for trading and risk
  • Algorithmic Trader (ML Focus): Developing automated trading strategies
  • AI Risk Analyst: Using machine learning for financial risk modeling
  • FinTech Product Innovator: Creating AI-driven financial technologies
  • Public Policy & Ethics AI Policy Analyst: Shaping policy frameworks for AI governance
  • Ethics & AI Specialist: Addressing ethical challenges in AI deployment
  • Technology Policy Advisor: Guiding governments on AI regulation
  • Entrepreneurship & Innovation AI Startup Founder: Building ventures in AI
  • robotics
  • or automation
  • Innovation Strategist (AI Focus): Leading AI-driven product innovation
  • Tech Venture Analyst: Supporting investments in AI startups

Book Free Session with Our Admission Experts

Admission Experts