2023/24
עברית
English
עברית
אתם משתמשים כרגע בגישת אורחים (
התחברות
)
HUJI
2023/24
ראשי
הקורסים שלי
76967 קורס השלמה במתמטיקה - אלגברה ליניארית
יחידות־הוראה
Chapter 11: Matrix decomposition and Least Squares...
תקציר יחידת-הוראה
►
Chapter 10: Diagonalization of Symmetric Matrices, Quadratic Forms (Linear Algebra and its Applications chapter 7.1-7.2)
◄
Chapter 12 - Recommended Reading: Least-square Problems and Applications to Linear Models; Singular Value Decomposition; Applications
בחירת פעילות Required materials
השלמה
סטודנטים נדרשים
סימון כבוצע
Required materials
בחירת פעילות Reading list chapter 11
Reading list chapter 11
דף תוכן מעוצב
►
Chapter 10: Diagonalization of Symmetric Matrices, Quadratic Forms (Linear Algebra and its Applications chapter 7.1-7.2)
מעבר ל...
הדף הראשי של הקורס
מבוא
General notes
References
Chapter 1: Vectors in Rn, Cn, Spatial vectors (Schaum chapter 1)
Chapter 2: Algebra of Matrices (Schaum chapter 2)
Chapter 3: Systems of Linear Equations (Schaum chapter 3)
Intermediate reading (No bullshit guide to Linear Algebra - chapters 4-6)
Chapter 4: Vector Spaces (Schaum chapter 4)
Chapter 5: Linear Mappings (Schaum chapter 5)
Chapter 6: Linear Mappings and Matrices (Schaum chapter 6)
Chapter 7: Inner Product Spaces, Orthogonality (Schaum chapter 7)
Chapter 8: Determinants (Schaum chapter 8)
Chapter 9: Diagonalization: Eigenvalues and Eigenvectors (Schaum chapter 9)
Chapter 10: Diagonalization of Symmetric Matrices, Quadratic Forms (Linear Algebra and its Applications chapter 7.1-7.2)
Chapter 11: Matrix decomposition and Least Squares Approximate Solutions (No bullshit guide to Linear Algebra chapter 6.6 and 7.7)
Chapter 12 - Recommended Reading: Least-square Problems and Applications to Linear Models; Singular Value Decomposition; Applications
Chapter 13 - Recommended Reading: Canonical Forms (Schaum chapter 10)
Lecture recording and lectures notes from the 2020-2021 ELSC course
Practice questions before the exam
Previous Exams
◄
Chapter 12 - Recommended Reading: Least-square Problems and Applications to Linear Models; Singular Value Decomposition; Applications