/Machine_Learning

A collection of Machine Learning Study

Machine_Learning & Algorithms

My personal collection of Machine Learning/Deep Learning

0. Master's Thesis: An alternative method of Concordance correlation random subspace method

Link: https://digitalcommons.usf.edu/etd/8442/

Main Objectives:

  • Learn how to write and edit an academic paper
  • Practice designing a machine learning project with clear objectives
  • Modify and develop algorithms in an open-source ML software, Weka (Java)

1. Jing Lin, Long Dang, Mohamed Rahouti, Kaiqi Xiong: ML Attack Models: Adversarial Attacks and Data Poisoning Attacks In-vehicle network security (Sep 2021)

Link: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003187158-2/machine-learning-attack-models-jing-lin-long-dang-mohamed-rahoutikaiqi-xiong

2. Dang, Long, Thushari Hapuarachchi, Kaiqi Xiong, and Jing Lin. "Improving Machine Learning Robustness via Adversarial Training."

In 2023 32nd International Conference on Computer Communications and Networks (ICCCN), pp. 1-10. IEEE, 2023.

Online learning. Coursera's Machine Learning (Matlab) (completed in Summer and Fall 2019)

Please refer to this Github link.

Online learning. Deep Learning Specialization (Python) (ongoing)

Github: https://github.com/saigontrade88/Coursera_Deep_Learning

Course's Website: https://www.coursera.org/specializations/deep-learning#courses

  • Course 1: Neural Networks and Deep Learning (completed). Please refer to the Github link.
  • Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (ongoing, completed week 1 out of 3). Please refer to the Github link.
  • Course 3: Structuring Machine Learning Projects (not started yet)
  • Course 4: Convolutional Neural Networks (ongoing, completed weeks 1, and 2 out of 4). Please refer to the Github link.
  • Course 5: Sequence Models (not started it yet)

Online learning. Stanford Algorithms Specialization (ongoing)

Courses's website: https://www.coursera.org/specializations/algorithms

  • Course 1: Divide and Conquer, Sorting and Searching, and Randomized Algorithms (Completed with certification).
  • Course 2: Graph Search, Shortest Paths, and Data Structures
  • Course 3: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
  • Course 4: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

Online learning. Stanford Algorithms Specialization (ongoing)

Courses's website: https://www.coursera.org/specializations/mathematics-machine-learning

  • Course 1: Mathematics for Machine Learning: Linear Algebra (Completed with certification).
  • Course 2: Mathematics for Machine Learning: Multivariate Calculus (ongoing)
  • Course 3: Mathematics for Machine Learning: PCA