Not too easy. Not too difficult.
Course Overview:
Linear algebra is used everywhere, and the applications are endless. Linear algebra is the reason we can use computers the way we do, (partly) the reason why the websites we are looking for appear first on Google, and so forth. This class outlines the basics of linear algebra and sets you up with a decent grasp of the elementary concepts such as matrix operations, factorization, linear transformations, and the like. Of course, these are all very basic topics, but these are the foundation for further mathematics.
Course highlights:
The highlights of the course was essentially the whole course. You NEED to understand the basics if you want to understand higher level branches, or move into more rigorous linear algebra courses. Not only is it the basics, but the applications of linear algebra are magnificent and self evident. As for what was learned was previously stated. To reiterate, this class is the basics. There is nothing complex to the course, other than being able to compute algorithms, essentially. It is up to the student to understand how these concepts were discovered and proved (which isn't required, but is very helpful and interesting).
Hours per week:
3-5 hours
Advice for students:
Read up on things that you can't comprehend. Go to office hours. Do the homework, as this really reinforces your understanding of the basic material (as well as being a decent part of your grade). This class is not the most fun, but it can easily be something of great value if you put a little effort in and make the connections with the topics being taught.
Not too easy. Not too difficult.
Course Overview:
Linear algebra is applicable in everyday life (programming, finance, etc.) and is also the foundation for much high level mathematics. Without a general understanding of linear algebra you will miss out on the intricacies that mathematics offers, as well as the ability to solve very common problems.
Course highlights:
Towards the end of the semester you begin learning more applications, such as Markov chains, basic stochastic processes, and the like. Professor Wallace also opens the end of the course to be driven by student interests as well, so if you are interested in AI, machine learning, neural networks, and so on (as I am) he is very capable of introducing the basics (as well as book and resource recommendations for further information). The beginning of the course is simply the basics of linear algebra (matrix/vector manipulation, operations, various theorems [invertible matrix theorem is a crucial one for the course]). After all, this is an Applied Linear Algebra course. There is only so much that can be taught, but without a doubt, exploring more specific applications of linear algebra is where the "boring stuff" (for some) becomes fascinating.
Hours per week:
3-5 hours
Advice for students:
Taking notes in class is essential, the text book is a decent resource, but is not the core of the course. Asking questions the instant you require clarification or are confused is a good idea, otherwise it is easy to get overwhelmed or lost in some lectures. The main goal of this course, though, is to gain a decent grasp of the fundamentals of linear algebra and how to apply it to (relatively basic) problems. The homework is very well setup, and there are often one question quizzes to ensure you understand the material. If you are lost, seek help immediately, Prof. Wallace does do a very decent job at explaining the material within the lectures, but the lectures shouldn't be the only time you are learning the material. Take good notes, ask questions when you feel the need, and stay on top of the material. By the time the test comes around, you should have full confidence!
Not too easy. Not too difficult.
Course Overview:
Pretty fair overall, do all of the homework
Course highlights:
Lots of stuff with Matrices and determinants
Hours per week:
3-5 hours
Advice for students:
Do all of the homeworks and do the practice exams