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ashkan-abbasi66 / Kickstart

Study route for learners in machine learning / deep learning / computer vision

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Note: this page is forked from here. I update this page contents regularly, and it does not similar to the original page.

Learning schedule

This learning schedule is sorted out for reseachers or whoever interested in machine learning / deep learning / computer vision.

Introduction

The skills you need to develop a machine learning / deep learning / computer vision project include:

  • Coding skills. Coding is not the objective, but the tool. Without the tool, nothing can be built.
  • Mathematics. Math is the foundation of machine learning that you can't evade, among which statistics and probability are the most important.
  • Machine learning algorithms, which is the focus of most research or competitions.
  • Paper reading and writing, which involves English proficiency and professionalisim. You may not necesssarily publish a paper on a journal or a conference. However, to keep up with others' work and report your work, you have to be familiar with how to read and write a paper.

BOOKS

Beginner

Quick intro to machine learning, no math but a very good overview about what ML is and what ML does. We recommend approx. 2 weeks for study. The MATLAB assignments are not longer recommended.

Intermediate

Now we need to expand our sight to the current research topics in machine learning / deep learning / computer vision.

By far, you should be familiar with the basic concepts of machine learning / deep learning / computer vision. You might need to participate in a real project in a lab at school (choose a reputed lab carefully) or in a IT company. You may also consider join a more advanced competition on Kaggle.

Here, we provide a PyTorch coding template in python for developing a real project.

Advanced

Don't rush to dig into these advanced courses. These courses are more specific for certain topics. Only after you have several project experiences, can these advanced courses help you build up a systematic sense of these topics.

  • Yida Xu (UTS)'s Probabilities and Machine Learning video link: Youtube Bilibili
  • Hung-yi Lee (NTU)'s GAN 2018 YouTube
  • Hung-yi Lee (NTU)'s Next Step of Machine Learning YouTube
  • CS 294-131: Trustworthy Deep Learning Homepage
  • CMU 10-708 PGM (19) by Eric Xing Homepage
  • Berkely Deep RL Bootcamp Homepage
  • CS294-158 Deep Unsupervised Learning Spring 2019 Homepage
  • Udacity's Cuda (Homepage)
  • Cousera's Programming Language Homepage
  • Udacity's Design of Computer Programs Homepage: How to approach programming problems and devise a solution is an essential skill for any Python developer - by Peter Norvig.

At this point, you have mastered the basic skill and knowledge required for machine learning / deep learning / computer vision research. But there are still so much unknown placed waiting for you to explore. What you learn here merely provides you with the way leading to those places. Begin your adventure now! And enjoy the beauty of maching learning!

Tools

TODO

Any advice or comments to improve this learning schedule is most welcomed.

Maintainer

Contributors

  • Jiancheng Yang who provides the primary study route and first start this project.
  • Linguo Li who provides the MNIST reference code and packages list.

About

Study route for learners in machine learning / deep learning / computer vision