Textbooks in Machine Learning

hyangki0119·2022년 3월 30일
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AuthorYearTextbook
Hastie, Tibshirani, Friedman2008The Elements of Statistical Learning : Data Mining, Inference, and PredictionO
Christopher M. Bishop2016Pattern Recognition and Machine LearningO
Ian Goodfellow, Yoshoua Bengio, and Aaron CourvilleDeep Learning : Adaptive Computation and Machine LearningO
Burkov, AndriyThe Hundred-Page Machine Learning BookO
Meor AmerA visual introduction to Deep Learning
Understanding Machine Learning(Cambridge University Press)
Witten Ian H., Frank Eibe, Hall Mark A.Data Mining : Practical Machine Learning Tools and TechniquesO
Mohri Mehryar, Rostamizadeh Afshin, Ameet TalwalkarFoundations of Machine Learning
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong2020Mathematics for Machine Learning
Python Machine Learning : Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2(Raschka, Sebastian)
Python Machine Learning : A Technical Approach To Python Machine Learning For Beginners(Eddison, Leonard)
Eli Stevens, Luca Antiga, and Thomas ViehmannDeep Learning with PyTorch
Sebastian Raschka, Yuxi Liu, Dmytro DzhulgakovMachine Learning with PyTorch and Scikit-Learn : Develop machine learning and deep learning models with Python
Deep learning in production(Sergios Karagianakos)
Machine Learning Engineering(Andriy Burkov)
Justin Solomon2015Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Main methods in ML

  • Supervised learning
    • Regression
      • Linear methods (Ridge, Lasso)
      • Basis Expansions (Polynomials, Splines, Wavelet)
      • Kernel methods (Radial Basis, Gaussian Processes)
      • Tree-based methods (Decision trees, Random forest, Gradient Boosting)
      • Support vector machine
      • Nearest-Neighbor Methods
      • Neural Network
    • Classification
      • Linear methods (Logistic regression)
  • Unsupervised learning
    • Association Rules
    • Cluster Analysis
    • Self-Organizing Maps
    • Principal Components Anaylsis
    • Multidimensional Scaling

  • Sampling
  • Ensemble (Model Averaging)

Hastie vs. Bishop

(Hastie, 2008)Both(Bishop, 2006)
Linear regression
Linear classification
Basis Expansions
Local Regression
Kernel Density Estimation and Classification
Kernel method
Radial Basis Function
Gaussian Processes
Model Assessment and Selection
LDA
FDA
SVMSparse kernel method
RVM
Neural networks
Vector QuantizationK-means
Mixtures of Gaussians
EM algorithm
k-NN
Association Rules
Approximate Inference
Sampling Methods
Spectral Clustering
Kernel Principal Components
Sparse Principal Components
PCA
Latent variable
Probabilistic PCA
EM algorithm
Kernel PCA
MDS
Random Forests
High-Dimensional
Markov Graphs
Undirected Graphical Models
Graphical modelBayesian Networks
Conditional Independence
Markov Random Fields
Markov Models
Hidden Markov Models
Linear Dynamical Systems
Ensemble LearningBoostingCombining Models
Bayesian Model Averaging

Objectives

  • 머신러닝의 주요 task는 무엇인가?
  • 각 기법의
    • Mathematical formulation
    • (If possible) Visual intuition
    • Example code in Python

[References]

20 Best Machine Learning Books for Beginner & Experts in 2022
8 Best Books in Machine Learning to Read in 2021
Best Machine Learning (ML) Books — Free and Paid — Editorial Recommendations for 2022
10 Best Machine Learning Textbooks that All Data Scientists Should Read
Best AI and Deep learning books to read in 2022
10 Best Machine Learning Textbooks that All Data Scientists Should Read

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