Machine Learning

To make machine learning and understand machine learning

17 Neural ODE

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Summary: Neural ODE
Pages: 1

16 Adversarial Models

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Summary: Adversarial models use a generator and discriminator
Pages: 3

15 Contrastive Models

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Summary: Contrastive self-supervised learning models can utilize more data
Pages: 5

14 Generative Models

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Summary: Generative self-supervised learning models can utilize more data
Pages: 5

14 Interpretability

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Summary: Interpretability of models
Pages: 1

13 Energy-based Model

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Summary: Energy-based model like Boltzmann machine is a special type of neural networks
Pages: 4

12 Classification

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Summary:
Pages: 3

11 Unsupervised Learning

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Tags:
#PCA #SVM
Summary:
Pages: 3

10 Artificial Neural Networks

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Summary: Artificial Neural Networks
Pages: 5

9 Ensemble Methods

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Summary:

9 Tree-based Methods

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Summary: Decision trees are a simple model for decision. Yet combined with other methods, decision trees can be quite powerful.
Pages: 3

8 Bayesian Methods

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Summary: Machine learning based on bayesian statistics
Pages: 4

7 Embedding

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Tags:
#word2vec
Summary: Embedding was one of the first ideas on computers and it is still the key component of machine learning
Pages: 2

6 Factorization

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Summary: Factorization
Pages: 3

5 Linear Models

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Summary: Linear models are very useful for baseline models.
Pages: 3

4 Machine Learning Performance

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Summary: Benchmarking the performance of models
Pages: 1

3 Feature Engineering

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Summary: In the industry, we spend a lot of time working on feature engineering.
Pages: 3

2 Machine Learning Basics

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Summary: Some basics of machine learning
Pages: 3

1 Machine Learning Overview

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Category: { Machine Learning }
References: - Mehta, P., Bukov, M., Wang, C. H., Day, A. G. R., Richardson, C., Fisher, C. K., & Schwab, D. J. (2019). A high-bias, low-variance introduction to Machine Learning for physicists. Physics Reports, 810, 1–124. - Shalev-Shwartz, S., & Ben-David, S. (2013). Understanding machine learning: From theory to algorithms. Understanding Machine Learning: From Theory to Algorithms - Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55 (10), 78–87. - Abu-Mostafa, Yaser S and Magdon-Ismail, Malik and Lin, Hsuan-Tien. Learning from Data. 2012. Available: https://www.semanticscholar.org/paper/Learning-From-Data-Abu-Mostafa-Magdon-Ismail/1c0ed9ed3201ef381cc392fc3ca91cae6ecfc698 - Deckert D-A. Advanced Topics in Machine Learning. In: Advanced Topics in Machine Learning [Internet]. Apr 2017 [cited 17 Oct 2021]. Available: https://www.mathematik.uni-muenchen.de/~deckert/teaching/SS17/ATML/
Summary: A brief overview of machine learning
Pages: 53