# A Brief Introduction to Machine Learning for Engineers

arXiv: https://arxiv.org/abs/1709.02840 · HTML: https://mlsanity.com/paper/1709.02840/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2017.

## Built on by (most cited first, 20 of 20)

- [Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck](https://mlsanity.com/paper/2106.01177/) (2021)
- [secureTF: A Secure TensorFlow Framework](https://mlsanity.com/paper/2101.08204/) (2021)
- [Semantic Information Recovery in Wireless Networks](https://mlsanity.com/paper/2204.13366/) (2022)
- [Bayesian Variational Federated Learning and Unlearning in Decentralized Networks](https://mlsanity.com/paper/2104.03834/) (2021)
- [Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning](https://mlsanity.com/paper/2111.01750/) (2021)
- [Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance](https://mlsanity.com/paper/2210.16519/) (2022)
- [Structured Machine Learning Tools for Modelling Characteristics of Guided Waves](https://mlsanity.com/paper/2101.01506/) (2021)
- [Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation](https://mlsanity.com/paper/2110.00414/) (2021)
- [Client Selection for Federated Bayesian Learning](https://mlsanity.com/paper/2212.05492/) (2022)
- [CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity](https://mlsanity.com/paper/2102.12756/) (2021)
- [Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization](https://mlsanity.com/paper/2201.08629/) (2022)
- [Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization](https://mlsanity.com/paper/2108.00785/) (2021)
- [Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition](https://mlsanity.com/paper/2203.12715/) (2022)
- [Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient](https://mlsanity.com/paper/2305.03571/) (2023)
- [Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study](https://mlsanity.com/paper/2509.07846/) (2025)
- [Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization](https://mlsanity.com/paper/2102.03280/) (2021)
- [Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo](https://mlsanity.com/paper/2112.09794/) (2021)
- [Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G](https://mlsanity.com/paper/2206.04967/) (2022)
- [Integrating Semantic Communication and Human Decision-Making into an End-to-End Sensing-Decision Framework](https://mlsanity.com/paper/2412.05103/) (2024)
- [TMIQ: Quantifying Test and Measurement Domain Intelligence in Large Language Models](https://mlsanity.com/paper/2503.02123/) (2025)

## Abstract (arXiv)

This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems. It introduces fundamental concepts and algorithms by building on first principles, while also exposing the reader to more advanced topics with extensive pointers to the literature, within a unified notation and mathematical framework. The material is organized according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, as well as directed and undirected models. This monograph is meant as an entry point for researchers with a background in probability and linear algebra.