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STEM textbooks used at top US universities
Anchored on the books required in CS, math, physics, biology, and engineering programs at MIT, Stanford, Berkeley, CMU, and Caltech.
MathematicsAbstract Algebra
David S. Dummit, Richard M. Foote
3rd Edition
StatisticsAll of Statistics: A Concise Course in Statistical Inference
Larry Wasserman
1st Edition
PhysicsAn Introduction to Mechanics
Daniel Kleppner, Robert Kolenkow
2nd Edition
PhysicsAn Introduction to Quantum Field Theory
Michael E. Peskin, Daniel V. Schroeder
1st Edition
StatisticsAn Introduction to Statistical Learning with Applications in R
Gareth James, Daniela Witten et al.
1st Edition
Computer ScienceArtificial Intelligence: A Modern Approach
Stuart Russell, Peter Norvig
4th Edition
Computer ScienceArtificial Intelligence: A Modern Approach
Stuart Russell, Peter Norvig
3rd Edition
Computer ScienceArtificial Intelligence: A Modern Approach
Stuart Russell, Peter Norvig
3rd Global Edition
ChemistryAtkins' Physical Chemistry
Peter Atkins, Julio de Paula et al.
11th Edition
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- Berkeley CS 164Programming Languages and Compilers
- Berkeley CS 170Efficient Algorithms and Intractable Problems
- Berkeley CS 188Introduction to Artificial Intelligence
- Berkeley CS 189Introduction to Machine Learning
- Berkeley CS 61AStructure and Interpretation of Computer Programs
- Berkeley CS 70Discrete Mathematics and Probability Theory
- Berkeley MATH 54Linear Algebra and Differential Equations
- Berkeley PHYSICS 7APhysics for Scientists and Engineers
- Caltech Ma 1Calculus of One and Several Variables
- Caltech Ph 1Classical Mechanics and Electromagnetism
- CMU 10-701Introduction to Machine Learning
- CMU 15-122Principles of Imperative Computation
- CMU 15-213Introduction to Computer Systems
- CMU 15-251Great Ideas in Theoretical Computer Science
- CMU 15-411Compiler Design
- CMU 21-241Matrices and Linear Transformations
- Cornell CS 2110Object-Oriented Programming and Data Structures
- Cornell CS 4820Introduction to Analysis of Algorithms
- Cornell MATH 2210Linear Algebra
- Cornell PHYS 2217Physics II: Electromagnetism
- Georgia Tech CS 1332Data Structures and Algorithms
- Georgia Tech CS 3510Design and Analysis of Algorithms
- Georgia Tech CS 6505Computability, Algorithms, and Complexity
- Georgia Tech CS 7641Machine Learning
- Michigan EECS 281Data Structures and Algorithms
- Michigan EECS 376Foundations of Computer Science
- Michigan EECS 482Introduction to Operating Systems
- Michigan MATH 217Linear Algebra
- MIT 18.06Linear Algebra
- MIT 18.100Real Analysis
- MIT 6.006Introduction to Algorithms
- MIT 6.035Computer Language Engineering
- MIT 6.046Design and Analysis of Algorithms
- MIT 6.867Machine Learning
- MIT 7.06Cell Biology
- MIT 8.01Physics I: Classical Mechanics
- MIT 8.02Physics II: Electricity and Magnetism
- Princeton COS 226Algorithms and Data Structures
- Princeton COS 340Reasoning About Computation
- Princeton MAT 215Single Variable Analysis with an Introduction to Proofs
- Princeton PHY 205Classical Mechanics
- Stanford CS 107Computer Organization and Systems
- Stanford CS 140Operating Systems
- Stanford CS 143Compilers
- Stanford CS 161Design and Analysis of Algorithms
- Stanford CS 221Artificial Intelligence: Principles and Techniques
- Stanford CS 229Machine Learning
- Stanford MATH 51Linear Algebra and Differential Calculus of Several Variables
- Stanford PHYSICS 41Mechanics
- UIUC CS 225Data Structures
- UIUC CS 374Introduction to Algorithms and Models of Computation
- UIUC MATH 416Abstract Linear Algebra
- UIUC PHYS 214Quantum Physics
- UT Austin CS 314Data Structures
- UT Austin CS 378Machine Learning
- UT Austin M 340LMatrices and Matrix Calculations
- UW CSE 373Data Structures and Algorithms
- UW CSE 421Introduction to Algorithms
- UW CSE 446Machine Learning
- UW STAT 340Introduction to Probability and Statistics
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