Author	Year	Textbook
Hastie, Tibshirani, Friedman	2008	The Elements of Statistical Learning : Data Mining, Inference, and Prediction	O
Christopher M. Bishop	2016	Pattern Recognition and Machine Learning	O
Ian Goodfellow, Yoshoua Bengio, and Aaron Courville		Deep Learning : Adaptive Computation and Machine Learning	O
Burkov, Andriy		The Hundred-Page Machine Learning Book	O
Meor Amer		A 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 Techniques	O
Mohri Mehryar, Rostamizadeh Afshin, Ameet Talwalkar		Foundations 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 Ong	2020	Mathematics 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 Viehmann		Deep Learning with PyTorch
Sebastian Raschka, Yuxi Liu, Dmytro Dzhulgakov		Machine 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 Solomon	2015	Numerical 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	SVM	Sparse kernel method RVM
	Neural networks
Vector Quantization	K-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 model	Bayesian Networks Conditional Independence Markov Random Fields
		Markov Models Hidden Markov Models Linear Dynamical Systems
Ensemble Learning	Boosting	Combining 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