Develop a mathematical intuition for how machine learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems.
Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning.
For each category of algorithm, you’ll go from math-first principles to a hands-on implementation in Python, exploring dozens of examples from across all the fields of machine learning. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Author(s): Vadim Smolyakov
Publisher: Manning Publications
Year: 2023
Language: English
Pages: 267
Machine Learning Algorithms in Depth MEAP V07
Copyright
Welcome
Brief contents
Chapter 1: Machine Learning Algorithms
1.1 Types of ML Algorithms
1.2 Why Learn Algorithms from Scratch?
1.3 Bayesian Inference and Deep Learning
1.3.1 Two Main Camps of Bayesian Inference: MCMC and VI
1.3.2 Modern Deep Learning Algorithms
1.4 Implementing Algorithms
1.4.1 Data Structures
1.4.2 Problem-Solving Paradigms
1.5 Summary
Chapter 2: Markov Chain Monte Carlo
2.1 Introduction to Markov Chain Monte Carlo
2.1.1 Posterior Distribution of Coin Flips
2.1.2 Markov Chain for Page Rank
2.2 Estimating Pi
2.3 Binomial Tree Model
2.4 Self-Avoiding Random Walk
2.5 Gibbs Sampling
2.6 Metropolis-Hastings Sampling
2.7 Importance Sampling
2.8 Exercises
2.9 Summary
Chapter 3: Variational Inference
3.1 KL Variational Inference
3.2 Mean-Field
3.3 Image Denoising in Ising Model
3.4 MI Maximization
3.5 Exercises
3.6 Summary
Chapter 4: Software Implementation
4.1 Data Structures
4.1.1 Linear
4.1.2 Non-Linear
4.1.3 Probabilistic
4.2 Problem-Solving Paradigms
4.2.1 Complete Search
4.2.2 Greedy
4.2.3 Divide and Conquer
4.2.4 Dynamic Programming
4.3 ML Research: Sampling Methods and Variational Inference
4.4 Exercises
4.5 Summary
Chapter 5: Classification Algorithms
5.1 Introduction to Classification
5.2 Perceptron
5.3 SVM
5.4 Logistic Regression
5.5 Naïve Bayes
5.6 Decision Tree (CART)
5.7 Exercises
5.8 Summary
Chapter 6: Regression Algorithms
6.1 Introduction to Regression
6.2 Bayesian Linear Regression
6.3 Hierarchical Bayesian Regression
6.4 KNN Regression
6.5 Gaussian Process Regression
6.6 Exercises
6.7 Summary
Chapter 7: Selected Supervised Learning Algorithms
7.1 Markov Models
7.1.1 Page Rank Algorithm
7.1.2 Hidden Markov Model
7.2 Imbalanced Learning
7.2.1 Undersampling Strategies
7.2.2 Oversampling Strategies
7.3 Active Learning
7.3.1 Query Strategies
7.4 Model Selection: Hyperparameter Tuning
7.4.1 Bayesian Optimization
7.5 Ensemble Methods
7.5.1 Bagging
7.5.2 Boosting
7.5.3 Stacking
7.6 ML Research: Supervised Learning Algorithms
7.7 Exercises
7.8 Summary
Chapter 8: Fundamental Unsupervised Learning Algorithms
8.1 Dirichlet Process K-Means
8.2 Gaussian Mixture Models (GMMs)
8.2.1 EM Algorithm
8.3 Dimensionality Reduction
8.3.1 Principal Component Analysis
8.3.2 t-SNE Manifold Learning on Images
8.4 Exercises
8.5 Summary
Chapter 9: Selected Unsupervised Learning Algorithms
9.1 Latent Dirichlet Allocation
9.1.1 Variational Bayes
9.2 Density Estimators
9.2.1. Kernel Density Estimator
9.2.2. Tangent Portfolio Optimization
9.3 Structure Learning
9.3.1. Chow-Liu Algorithm
9.3.2. Inverse Covariance Estimation
9.4 Simulated Annealing
9.5 Genetic Algorithm
9.6 ML Research: Unsupervised Learning
9.7 Exercises
9.8 Summary
Chapter 10: Fundamental Deep Learning Algorithms
10.1 Multi-Layer Perceptron
10.2 Convolutional Neural Nets
10.2.1 LeNet on MNIST
10.2.2 ResNet Image Search
10.3 Recurrent Neural Nets
10.3.1 LSTM Sequence Classification
10.3.2 Multi-Input Model
10.4 Neural Network Optimizers
10.4.1 Exercises
10.5 Summary
