csci 2033
C S C I 2 0 3 3
Assignments and exams
| |
HW1 |
HW2 |
Exam 1 |
HW3 |
HW4 |
Exam 2 |
HW5 |
HW6 |
Final |
| Posted |
Jan 24 |
Feb 9 |
Mar 2 |
Mar 2 |
Mar 14 |
Apr 6 |
Apr 6 |
Apr 20 |
May 10* |
| Due |
Feb 9 |
Feb 23 |
Mar 2 |
Mar 11 |
Mar 30 |
Apr 6 |
Apr 20 |
May 4 |
May 10* |
* Final exam : May 10th (Tue.) 8:00am -- 10:00am -- Keller Hall 3-210
Final exam -- General Information
Exam solution key [Login required]
- The exam is from 8:00am to 10:0am, Tuesday May 10th,
in the same room as the lecture room (Keller Hall 3-210). Make
sure to bring your student ID.
- The exam is closed book but you will be allowed to have
a 2-page (1 sheet back-to-back) formula sheet. This is your own sheet,
and
you can write anything on it -- and use any font. [Only restriction
is the size of the sheet which is the regular 8"x11" format.]
No other documents will be allowed. Make sure to
mark your name down on your formula sheet.
- You may be asked simple generic questions about matlab in this
exam (however: no scripts or codes).
- No calculators will be allowed [they wont be needed].
Clearly: no laptops no cell-phones,...;
- Material covered You may be asked questions on all the
material seen in class from the beginning.
However, there will be more questions
on topics seen since the second mid-term. As an example: you may have
one question on the material seen up to mid-term 1,
one question on the material seen up to mid-term 2,
and three questions on linear transformations,
determinants, eigenvalues, and the SVD.
- For those interested here is the information I posted
for
the first and
the second mid-terms [includes the topics].
- Topics seen since Mid-term2 and related book sections
(for review).
- The QR factorization; Gram-Schmidt algorithm; [Sec: 4.4 ]
- Least-squares with QR [Sec: 4.4]
- Determinants; properties; definition; general formula.
cofactor expansion [Sec: 5.1, 5.2]
- Determinants: Cramer's rule;
applications to areas and volumes [Sec: 5.3]
- Linear transformations; change of bases. [Sec: 7.1, 7.2, 6.6]
- Eigenvalue problems; eigenvalues and eigenvectors; How to compute
them; diagonalizable matrices; [Sec: 6.1, 6.2]
The symmetric case. [Sec: 6.4]
- The Singular Value Decomposition (SVD). [Sec: 6.7]
Computing singular values. Applications of the SVD.
Second mid-term exam
-
Mid-Term 2 Solution key (login required)
MT2_sol.pdf
(PDF format)
First mid-term exam
-
Mid-Term 1 Solution key (login required)
MT1_sol.pdf
(PDF format)