Please note that these topics are prerequisites for the course and it is assumed that you are already familiar with most of these topics. Use these resources to quickly bring yourself up to speed with any areas that you are not comfortable with.

###### Linear Algebra

- I highly recommend the Linear Algebra video series by Grant Sanderson.
- For more in-depth treatment of topics see Gilbert Strang's lectures at MIT.

###### Multivariate Calculus

See the Khan Academy video series. More specifically, these specific videos are very relevant to the course.- Multivariable Function
- Points and Vectors
- Partial Derivative
- Partial Derivative Graph
- Partial Second Derivative
- Gradient
- Gradient Graph
- Directional Derivative
- Directional Derivative Graph
- Why the gradient is the direction of steepest ascent
- Jacobian Prerequisite Knowledge
- Jacobian Matrix
- Computing a Jacobian matrix
- Hessian matrix

###### Basic Probability

- I highly recommend this for interactively learning about Basic Probability.
- This four part video is a good summary of important concepts from probability we will use Part 1, Part 2, Part 3, and Part 4.
- For more in-depth coverage of the material see the Basic Probability course from MIT.

###### Python

- Video on (
__we will use Python 3.7 in this course__) installation of Python and Jupyter Notebook.__Assignments must be submitted in python__. - A great interactive tutorial on Python. You can open it in Google Colab and interact with the code.
- Video on running Google Colab. This is not required for the course, but it is good to be aware of it and optionally use it if you like.
- An in-depth video for learning Python. Use the slider in the video to go to the topics that you are of interest to you.

###### Other Resources on Machine Learning and Deep Learning

- Although we will cover the basics of deep learning, you can learn more on your own. This interactive book on deep learning is one good source.
- A deep learning book by Ian Goodfellow et al.
- My course on Machine Learning.