The midterm exam will take place on Thursday, 10/18, at LO 1322 during regular lab hours. Here is a Midterm review sheet I with practice Linear Algebra prblems
How to install the software you will
need for the course
LaTeX - a powerful typesetting system that will allow you to create pdf documents
as required for your homework and lab assignments. To download and install it, you may
follow the instructions posted by Prof. B. Shapiro,
if you are a Mac user, you may also try to download and install TeXShop
Canopy - a Python distribution for scientific computing, it contains all you will need for this course.
class starts on Tuesday, Aug. 28th, at
10.00. We'll meet at LO 1322.
our first lab meeting will be held at
LO 1322 on Thursday, Aug. 30th, at 10.00.
meeting times and office hours
about the course
This course introduces machine learning with an emphasis
on the mathematical principles and computational techniques
required for designing and implementing algorithms that allow
computers to learn from data and then make a determination or
prediction about something in the world. The mathematical content
includes selected topics from linear algebra, probability, statistics,
information theory, and numerical computation. The essentials of
computer programming are developed using Python, a powerful
scripting language. This course requires a
significant time commitment.
although not listed as a pre or corequisite, it is strongly suggested
that you have completed an introductory course in computer programming
Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press
Visit the book's reference page
A list of topics, class notes, and other course materials are available
I may also refer to topics in the following texts from time to time
and the links below
materials (lecture notes, lab notes, slides, etc.)
Remarks: (1) Any cell phone in your possession at the time of the exam must be visible and on the
floor; (2) you will need to turn in the exam if you leave the room, so go to the bathroom beforehand; (3) you must leave your ID on top
of the desk during exams
It is suggested and encouraged that you work on assignments and study for quizzes and exams with other
students in the class, however, any graded work should be completed independently. Any indication of shared solutions, plagiarism,
or any other dishonest conduct will be investigated thoroughly and, if confirmed, will result in a
failing grade in the course and any further action contemplated by University policy. If you are
unsure what I mean by this, please ask and/or check the University Catalog. According to CSUN's policy, a
grade that is sanctioned due to academic dishonesty cannot be replaced by subsequent course grades.
computer software and
programming assignments and computer lab exercises will be done with Python,
a powerful scripting language with simple and compact syntax. We'll make extensive use of packages such as
SciPy, and matplotlib,
which add functionality to Python by providing data structures and mathematical
objects (e.g., matrices) and plotting capabilities.
The computers in the lab (LO 1322) have a full Enthought
Python Distribution (EPD) installed and you are encouraged to download and install your own.
- LaTeX. Homework
assignments and computer lab reports should be typed
a powerful typesetting system that gives the user a
great deal of control over the formatting of the
documents and provides commands to type mathematical
expressions and symbols.
The computers in the lab have a complete LaTeX
installation and you are encouraged to download and
install your own (see the links
Written homework will be assigned in
class evey other week and collected via canvas. Students
may work together in groups and discuss the homework
problems with each other, but each student should write up
and submit their own solutions. Homewrok solutions must be
submitted online in pdf format (generated with LaTeX).
Programming assignments will be posted here every other week. Typically, these assignments will
consist of a list of problems -related to the topics discussed in class and lab that week- to be solved using
Python. While group work is encouraged, each student is expected to write his/her own code and to submit a report
with his/her own results and conclusions. Lab assignments will be submitted and collected online using canvas.
Here are some links to online materials
and references relevant to the course. If you know of
anything worth posting here, please email it to me.
check here for examples from lecture and
lab (you may need to right-click on the link and choose
save file rather than just clicking on it)
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