# Difference between revisions of "VS265: Homework assignments"

### From RedwoodCenter

(→Lab #1, due Tuesday, September 16 at beginning of class) |
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* [http://redwood.berkeley.edu/vs265/data.mat data.mat] | * [http://redwood.berkeley.edu/vs265/data.mat data.mat] | ||

<!--[http://redwood.berkeley.edu/w/images/e/ef/Hw1_soln_bilenko.pdf Solution.pdf] --> | <!--[http://redwood.berkeley.edu/w/images/e/ef/Hw1_soln_bilenko.pdf Solution.pdf] --> | ||

+ | |||

+ | |||

+ | ==== Lab #2, due Tuesday, September 23 at beginning of class ==== | ||

+ | * [http://redwood.berkeley.edu/vs265/lab2.pdf lab2.pdf] | ||

+ | * [http://redwood.berkeley.edu/vs265/apples.mat apples.mat] | ||

+ | * [http://redwood.berkeley.edu/vs265/oranges.mat oranges.mat] | ||

+ | * [http://redwood.berkeley.edu/vs265/lab2s.m lab2s.m] or [http://redwood.berkeley.edu/vs265/lab2s.txt lab2s.py] | ||

+ | * [http://redwood.berkeley.edu/vs265/apples2.mat apples2.mat] | ||

+ | * [http://redwood.berkeley.edu/vs265/oranges2.mat oranges2.mat] | ||

+ | |||

+ | ''For Python you can use [http://redwood.berkeley.edu/vs265/apples-oranges.npz apples-oranges.npz]'' | ||

+ | In [1]: import numpy as np | ||

+ | In [2]: d = np.load('apples-oranges.npz') | ||

+ | In [3]: d.keys() | ||

+ | Out[3]: ['oranges2', 'apples2', 'apples', 'oranges'] |

## Revision as of 21:39, 16 September 2014

Students are encouraged to work in groups, but turn in assignments *individually*, listing the group members they worked with.

**Submission instructions**:
email both a PDF of your solutions as well as your code (.m or .py files) as attachments to:

vs265 AT rctn.org

You can hand in a paper copy of your solutions before class, but you still have to email your code to the address above before the assignment is due.

## Contents

# Resources

## Matlab

Student version of Matlab ($50) may be obtained here.

There is an excellent guide to Matlab by Kevin Murphy on the web: http://code.google.com/p/yagtom/

## Python

Fernando Perez at the Brain Imaging Center has an excellent set of resources on Python for scientific computing. You will likely find the "Starter Kit" particularly useful.

Also, a great starting point for all scientific python is using Anaconda [1]

# Assignments

#### Lab #1, due Tuesday, September 16 at beginning of class

#### Lab #2, due Tuesday, September 23 at beginning of class

*For Python you can use apples-oranges.npz*

In [1]: import numpy as np In [2]: d = np.load('apples-oranges.npz') In [3]: d.keys() Out[3]: ['oranges2', 'apples2', 'apples', 'oranges']