This repository contains common materials for delivery of the Computer Programming for Data Science and AI course in the Asian Data Science and Artificial Intelligence Master's curriculum (dsai.asia).
--
Some resource worth mentioning:
- Lectures/Starters/0 - Reading Roadmap
- For those who wants to know what papers to read. I have listed ONLY the most important papers you need to read in the field of machine learning
- Lectures/Starters/0 - Installation
- For newbies who have trouble installing Python and other tools
- Lectures/Starters/0 - Course Notations
- Understanding notations is the first step towards conquering math, so take a look and familiarized with it
- Lectures/Advanced
- These are lessons I do not intend to teach given the time limit. It is intended for self-study.
- Self-Exercises
- Every Lecture has a lab folder containing the assessment and solution. Anyhow, I also compile a list of possible exercises for student's self learning inside the Self-Exercises folder.
- AIT-2020
- The file "0. Course Introduction.ipynb" contains how I run the course. This course is a 15 weeks course, each week having two labs of 3 hours each. Each lab always end with the assessment and solution.
I would also like to give credits to several githubs that I have revised to create this:
- https://github.com/drgona/Pytorch_bootcamp_Udemy
- https://github.com/SethHWeidman/DLFS_code
- https://github.com/jakevdp/PythonDataScienceHandbook
The course is structured into 3 big components, mostly focusing on preprocessing and modeling perspectives:
(Note: For detailed information, please read "0 - Course Introduction")
Focus on getting started.
- Python
- Numpy
- Pandas
- Matplotlib
- Sklearn
Focus on understanding the math + coding via coding from scratch
- Linear regression
- Polynomial regression
- Regularization
- Logistic regression
- Naive Gaussian
- Support Vector Machines
- Decision Trees
- K-Nearest Neighbors
- Bagging
- Random Forests
- Boosting - AdaBoost, Gradient Boosting
- K-means
- Gaussian Mixture Models
- Principal Component Analysis
- Manifold Learning
- Momentum
- Batch Norm
- Dropout
- Decay Learning Rate
- Glorot Initialization
- Activation Functions
- Basics
- ANN
- CNN
- RNN