School of Engineering
Department of Computer Science

Undergraduate Focus Area: Artificial Intelligence

The Artificial Intelligence Focus Area in the Computer Science Major  
Department of Computer Science  
Tufts University  
Last updated by Matthias Scheutz on April 12, 2024

Overview

The crux of Artificial Intelligence (AI) is to understand how machines can learn to autonomously perform different types of tasks that typically require some intelligence on the human side (e.g., object classification and identification, task scheduling and planning, game playing, decision-making, natural language understanding, etc.). The purpose of this focus area is to provide you with breadth and depth in the broad area of artificial intelligence focusing on both statistical as well as symbolic approaches with a strong technical foundation in computer science. This focus area applies equally well for Arts and Sciences (A&S) and School of Engineering (SoE) students.

The Computer Science Core

  1. Introduction to Computer Science (CS 11)
  2. Data Structures (CS 15)
  3. Machine Structure & Assembly Language Programming (CS 40)
  4. Discrete Mathematics (CS 61)
  5. Programming Languages (CS 105)
  6. Algorithms (CS 160)
  7. Theory of Computation (CS 170)

The AI Core

  1. Artificial Intelligence (CS 131)
  2. Machine Learning (CS 135)
  3. Ethics in AI, Robotics, and HRI (CS 139)

AI Electives

Pick at least three courses from the list below:

  1. Computational Models in Cognitive Science (CS 134)
  2. Statistical Pattern Recognition (CS 136)
  3. Deep Neural Networks (CS 137)
  4. Reinforcement Learning (CS 138)
  5. Probabilistic Robotics (CS 141)
  6. Artificial Intelligence: Algorithms, Ethics, and Policy (CS 150-AEP)
  7. Computer Vision (CS 150-CVI)
  8. Deep Graph Learning (CS 150-DGL)
  9. Epistemic Planning (CS 150-EP)
  10. Logic for AI (or CS + AI) (CS 150-LAI)
  11. Trust in Human-Robot Interaction (CS 150-THR)
  12. Bayesian Deep Learning (CS 152-BDL)
  13. Cognitive Architectures Age of Foundation Models (CS 152-CAFM)
  14. Natural Language Processing (COMP 152-NLP)

Capstone

To be successful in AI in the future (in academia and industry alike), you will need to be able to demonstrate practical hands-on experience with AI algorithms and problems. You can fulfill the capstone de facto requirement in our AI focus area by either doing a year long senior capstone project via CS 97 and CS 98 or doing a thesis in AI via CS 197.