Linear Algebra Refresher Course

How much linear algebra prep should I do for harder courses?

Newly admitted student here. I haven't taken linear algebra in almost 8 years and I'm considering eventually taking courses like CV, HPC, AI, ML, all of which seem to have some element of LA.

I want to brush up on my knowledge in this area and am looking for good resources. Do you think the Linear Algebra Refresher Course on Udacity is sufficient or should I dive into Linear Algebra through MIT OCW?

I'm willing to go through the latter if people think it's worth it, but I'm afraid it will take a lot of time when I'm already trying to study discrete math and proofs.

Like the other commenter mentioned, there are a ton of college courses that you can take online.

Looks like Coursera has a Linear Algebra course that happens to start today :D https://www.coursera.org/learn/linear-algebra-machine-learning

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I also like Udacity because most of their courses are free, the videos are literally hosted on youtube, and they like to go in-depth on a lot of topics. I did their DS/Algos course a while back, it was a good starting point for my last job search since it covered the basics well.

Looks like they has a free Linear Algebra Refresher course although I haven't taken this course myself: https://www.udacity.com/course/linear-algebra-refresher-course--ud953

Course ends May 31, 2018 for anyone wondering.

Did you know MATLAB going in? I don't really want to re-learn MATLAB just to refresh my linear algebra knowledge. I wonder how it compares to something like this: https://www.udacity.com/course/linear-algebra-refresher-course--ud953

There is a linear algebra refresher with Python course on udacity which should provide you adequate background:

https://www.udacity.com/course/linear-algebra-refresher-course--ud953

There's Chris Pryby's "Linear Algebra Refresher" course that was at one time being recommended to OMSCS students (and maybe still is): https://www.udacity.com/course/linear-algebra-refresher-course--ud953

I took this and didn't think it was very good. I've had my eye on this: https://www.coursera.org/learn/linear-algebra-machine-learning but have not taken it so can't comment on its quality.

If you mean accredited courses for credit, I haven't looked.

Help critique my ML curriculum!

Hi everyone! I've been doing some research on how to get started on machine learning as a beginner and have come up with the curriculum below for myself to follow. It would be great if those proficient in ML, or even those currently learning, help to critique or provide suggestions. Additional courses, literature, programs, or advice would be greatly appreciated! It may be difficult to critique without having firsthand experience of each course or program, but I'm more concerned the with subject matter included than the courses themselves. As I go through each course, I may decide to change them if I feel that it is not pertinent to the goal or lacking in quality. There's no way I will become an expert after this, but I'm simply looking for a good starting point. I hope this will help others as well.

 

As a point of reference, I've graduated with a B.S. in Mechanical Engineering, have taken several math courses (calculus, linear algebra, differential equations), and have taken a few programming courses (CS50, Intro to Comp Sci on eDX, CS 101 at University). I'm currently working part-time as a mechanical designer / energy analyst and have 40-50 hours a week to spare.

 

For the curriculum, I've broken it up into 5 sections (programming, statistics, linear algebra, data science, and machine learning) which will be overlapping. I plan on taking 3 courses at a time starting with Udacity's Intro to programming, Stanford's probability and stats, and Udacity's linear algebra refresher while reading "An Introduction to Statistical Learning."

 

Here it is!

 

Curriculum:

Programming

1. Udacity Introduction to programming https://www.udacity.com/course/intro-to-programming-nanodegree--nd000
2. Learn the basics of programming through HTML, CSS, and Python.

Statistics

1. Stanford Online: Probability and Statistics https://lagunita.stanford.edu/courses/course-v1:OLI+ProbStat+Open_Jan2017/about

2. Broken into four sections: exploratory data analysis, producing data, probability, and inference.

3. Stanford Online: Statistical Learning http://online.stanford.edu/course/statistical-learning-winter-2014

4. Introductory-level course in supervised learning, with a focus on regression and classification methods.
5. Lectures cover all the material in An Introduction to Statistical Learning, with Applications in R.

Linear Algebra

1. Udacity Linear Algebra Refresher Course with Python: https://www.udacity.com/course/linear-algebra-refresher-course--ud953

2. UAustinX Linear Algebra - Foundations to Frontiers edx.org/course/linear-algebra

3. Connections between linear transformations, matrices, and systems of linear equations.
4. Partitioned matrices and characteristics of special matrices.
5. Algorithms for matrix computations and solving systems of equations. Vector spaces, subspaces, and characterizations of linear independence. *Orthogonality, linear least-squares, eigenvalues and eigenvectors

Data Science

1. Udacity Introduction to Data Science https://www.udacity.com/course/intro-to-data-science--ud359

2. Focuses on Data Manipulation, Data Analysis with Statistics and Machine Learning, Data Communication with Information Visualization, and Data at Scale -- Working with Big Data

3. Udacity Data Science Nano-degree https://www.udacity.com/course/data-analyst-nanodegree--nd002

4. Learn to organize data, uncover patterns and insights, make predictions using machine learning, and clearly communicate critical findings.

5. (optional) UCSD Data Science Micro-masters Program (4 course program) https://www.edx.org/micromasters/data-science

6. Four courses: Python for Data Science, Statistics and Probability in Data Science using Python, Machine Learning for Data Science, and Big Data Analytics Using Spark.

Machine Learning

1. Udacity Intro to Machine Learning https://www.udacity.com/course/intro-to-machine-learning--ud120

2. How to extract and identify useful features that best represent data, learn a few of the most important machine learning algorithms, and how to evaluate the performance machine learning algorithms.

3. Coursera Machine Learning by Andrew Ng https://www.coursera.org/learn/machine-learning

4. This course provides a broad introduction to machine learning, data-mining, and statistical pattern recognition.

5. (with job guarantee?) Udacity Machine Learning Nano-degree https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

6. Apply predictive models to massive data sets in fields like finance, healthcare, education, and more.

 

Literature:

1. An Introduction to Statistical Learning http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf

2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf

 

Other (after curriculum):

https://www.reddit.com/r/MachineLearning/wiki/index

 

Let me know your thoughts! Have you taken one of these courses before? Think there's too little programming or know of a better course/program? Chime in! Would be really grateful for any feedback.

Big Thanks!

-sleepyowl

I guess the refresher course on linear algebra did come to fruition:

https://www.udacity.com/course/linear-algebra-refresher-course--ud953

If you plan on doing ML stuff I highly recommend this (it's very quick) and you can spend more time where you feel you lack understanding: https://www.udacity.com/course/linear-algebra-refresher-course--ud953