This is a rough plan only and there will be changes throughout the semester. • indicates the approximate difficulty level				textbook problems (p1 = practice problem #1; 1.s - supplementary exercise chapter 1) to check your answers, bring them to office hours or email a photo to Dr. Pang
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		Monday	Thursday	check your understanding	skill	* conceptual, medium difficulty	* conceptual, challenging	out of syllabus, for interest
Week 1	5 September	•1.1 solving system of linear equations (matrix notation), examples of uniqueness/ existence	•1.2 RREF algorithm	1.1: p1, 23ab, 24abcd 1.2: 2, 5, 6, 15, 16, 21, 22abcde, 23, 25, 26, 27, 29-31	1.1: p2-4,1-4, 7-18, 20-22 1.2: p2, p3, 3, 4, 7-14, 19, 20	1.1: 25-32
Week 2	12 September		•1.3 vectors, span
Week 3	19 September	••1.4 matrix-vector multiplication, existence of solutions theorem	••1.5 parametric description of solutions, null space
Week 4	26 September	•••1.7 linear independence	•••1.7 linear independence, column space
Week 5	3 October	••1.8-1.9 linear trasformations, matrix of a linear transformation	•1.8 examples of linear transformations, ••1.9, one-to-one and kernel
Week 6	10 October	•2.1 matrix operations	••2.2 inverse of a matrix
Week 7	17 October	•••2.2, 2.3 invertible matrix theorem	•3.1-3.3 properties of determinants
Week 8	24 October	•••2.8 subspaces,  bases	•2.8 bases for null space, column space, row space
Week 9	31 October	•••2.9 dimension, basis theorem	•••2.9 rank
Week 10	7 November	•5.1, 5.2 eigenvectors and eigenvalues	••5.3 diagonalisation
Week 11	14 November	•6.1 inner product, length, orthogonality, pythagoras theorem, orthogonal complements	••6.5, 6.6 least squares, application to regression
Week 12	21 November	•••6.2, 6.3 orthogonal sets, orthogonal projections	•••6.3 orthogonal projections as a linear transformation, •6.4 gram schmidt
Week 13	28 November	••6.2 orthogonal matrices	•••abstract vector spaces