ml-techniques

A collection of machine learning codes in python's numpy and tensorflow.

Techniques

Clustering

• k-means [19 Dec 2020]
  • k-means with farthest-first heuristic (ffh) [11 Jan 2021]
  • k-means++ [10 Jan 2021]
• Spectral Clustering
  • Unnormalized Spectral Clustering [23 Dec 2020]

Manifold Learning

• PCA [9 Jan 2021]
• Classical MDS [22 Jan 2021]
• Fast ICA [23 Jan 2021]

Linear Models

• Logistic Regression [13 Jan 2021]
  • l2 regularization [15 Jan 2021]
• Linear Regression [16 Jan 2021]
  • Ridge Regression [18 Jan 2021]
  • LASSO [20 Jan 2021]

Bayesian Models

• Bayesian Linear Regression [19 Jan 2021]

Sampling Methods

• Importance Sampling [21 Jan 2021]

Requirements

Install requirements.txt file to make sure correct versions of libraries are being used.

• Python 3.8.x
• Jupyterlab==2.2.9
• Numpy==1.19.4
• Opencv-python==4.4.0.46
• Pandas==1.1.1
• Seaborn==0.11.0
• Scikit-learn==0.24.0
• Tensorflow==2.4.0
• Tensorflow-datasets==4.1.0
• Tensorflow-probability==0.11.1
• cvxpy==1.1.7

Resources

• Daumé III, Hal. "A course in machine learning." Publisher, ciml. info 5 (2012): 69.
• Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
• Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
• Miller, Jeffrey W. mathematicalmonk youtube lecture study page Machine Learning Playlist.

License

The MIT License (MIT)

Copyright (c) 2020 Peratham Wiriyathammabhum