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MATH-1554 Linear Algebra

This is the page for the coordinated course MATH-1554 Linear Algebra Studio Sections L02 at Georgia Tech. Here you can find some resources that may help you review.

My office hour in Fall 2024 is 1:30-2:30pm every Tuesday in Math Lab at Clough 280. If this time doesn’t work for you, you can still go to the Math Lab to get help any questions about MATH-1554. You can find the detailed schedule about Math Lab here.

However, you are always welcomed to schedule a time to meet with me either in-person or online. My email is rhuang346@gatech.edu, and you can check my schedule at MY SCHEDULE tab above.

Summarized Notes from Previous Studios

Section NumberTopicsNotes
1.1System of Linear Equationsw1-1
1.2Row Reduction and Echelon Formsw1-2
1.3-1.4Vector Equations
The Matrix Equation Ax=b
w2-1
1.5Solution Sets of Linear Systemsw2-2
1.7Linear Independencew3-1
1.8Introduction to Linear Transformationsw3-2
1.9-2.1The Matrix of a Linear Transformation
Matrix Operations
w4-1
1.1-1.5, 1.5-2.1Notes from studios before Midterm #1 in one filem1
2.2-2.3Inverse of a Matrix
characterizations of Invertible Matrices
w5-1
2.4-2.5Partitioned Matrices
Matrix Factorization
w5-2
2.8-2.9Subspace of R^n
Dimension and Rank
w6-1
3.1-3.2Introduction to Determinants
Properties of Determinants
w6-2
3.2-3.3Properties of Determinants
Cramer’s Rule, Volume, and Linear Transformation
w7-1
4.9 (5.9)Applications to Markov Chainsw7-2
5.1-5.2Eigenvectors and Eigenvalues
The Characteristic Equation
w8-1
2.2-2.5, 2.8-2.9,
3.1-3.3, 4.9 (5.9), 5.1-5.2
Notes from studios before Midterm #2 in one filem2
5.3Diagonalizationw9-2
5.5, 6.1Complex Eigenvalues
Inner Product, Length, and Orthogonality
w10-1
6.2Orthogonal Setsw10-2
6.3-6.4Orthogonal Projections
Gram-Schmidt Process
w11-1
6.5-6.6Least-Squares Problems
Machine Learning and Linear Models
w11-2
5.3, 5.4, 6.1-6.6Notes from studios before Midterm #3 in one filem3
10.2Page Rankw13-1
7.1-7.2Diagonalization of Symmetric Matrices
Quadratic Forms
w13-2
7.3Constrained Optimizationw14-1
7.4The Singular Value Decompositionw14-2
1.1-1.5, 1.5-2.1,
2.2-2.5, 2.8-2.9,
3.1-3.3, 4.9 (5.9),
5.1-5.4, 6.1-6.6,
7.1-7.4, 10.2
All notes from studiosall notes
Notes from past studios

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