Principal Component Analysis (PCA)

Linear Algebra

Gaussian Discriminant Analysis

Support Vector Machine (SVM)

Regularization (mathematics)

Expectation Maximization

Unsupervised learning

Prompting

Exponential family

Weighted Least Squares

Generalized Linear Models

Kernel (operating system)

Neural network

Feature / Model Selection

Factor Analysis

Self-supervised Learning

Instruct Tuning

Supervised learning

Newton's Method

Probability

NumPy

Bias - Variance

Gaussian Mixture Models (GMM)

Independent Component Analysis (ICA)

In-context Learning

Multi-class Classification

Python

Backpropagation

K-means clustering

Large Language Model (LLM)

Reinforcement Learning from Human Feedback (RLHF)

Logistic Regression

CS 229: Machine Learning Stanford University Winter 2023 This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.  This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.  Students are expected to have the following background:

- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy. (CS106A or CS106B, CS106X.)
- Familiarity with probability theory. (CS 109, MATH151, or STATS 116) 
- Familiarity with multivariable calculus and linear algebra (relevant classes include, but not limited to MATH 51, MATH 104, MATH 113, CS 205, CME 100.) Stanford Math 51 course text can be found [here](https://web.stanford.edu/class/math51/stanford/math51book.pdf). 

CS 229: Machine Learning

Machine Learning

Property-preserving lossy compression

Nearest Neighbor

k-d tree

L1 regularization

Matrix compression

Laplacian of a graph

Reservoir sampling

Markov chain Monte Carlo (MCMC)

Majority elements

Distance-preserving compression

PAC Guarantees

Linear-Algebraic Techniques

De-noising

Eigenvectors/eigenvalues

Markov's inequality

The Fourier Perspective

Linear programming

Dimensionality Reduction

Approximate heavy hitters

Locality-Sensitive Hashing (LSH)

MinHash

Matrix completion

Spectral embeddings

Chebyshev‘s inequality

Convolution

Convex optimization

Empirical risk minimization

Bloom filters

Jaccard Similarity

Polynomial embedding

Maximizing variance

Uniqueness of low-rank tensor factorizations

Graph coloring

Importance Sampling

Sparse Vector/Matrix Recovery

Privacy Preserving Computation

Count-min sketch

Euclidean Distance

Johnson-Lindenstrauss Transform

Random projection

Power iteration algorithm

Jenrich's algorithm

Spectral clustering

Markov Chains

Compressed Sensing

Differential privacy

Similarity Search

Lp distance

Generalization

L2 regularization

Singular Value Decomposition (SVD)

Spectral Graph Theory

Interpretations of the second eigenvalue

Stationary distributions

Mathematical Programming

CS 168: The Modern Algorithmic Toolbox Stanford University Spring 2022 CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.  This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include modern techniques in hashing, dimension reduction, linear and convex programming, gradient descent and regression, sampling and estimation, compressive sensing, and linear-algebraic techniques (principal components analysis, singular value decomposition, spectral techniques).  CS107 and CS161, or permission from the instructor. 

CS 168: The Modern Algorithmic Toolbox

Theoretical Computer Science

Decision Tree Learning

Margins

Naive Bayes

Maximizing Conditional Likelihood

Kernelizing Algorithms

Primal and Dual Forms

Finite Hypothesis Classes

k-fold cross-validation

Convolutional neural network (CNN)

Objective-Based Clustering

Semi-Supervised Learning

Learning Linear Separators

Maximum Likelihood Estimation (MLE)

Bag of Words

Computer Vision

Generalization and Overfitting

Model Selection and Regularization

Markov Decision Process (MDP)

Minimizing Squared Error

Deep Networks

Adaboost

Learning Representations

Active Learning

Co-training

Q-learning

Overfitting

Conditional Independence

Gradient descent

Kernelizing Perceptron

Kernelizing SVM

VC Dimension Based Bounds

Linear regression

Boosting

Hierarchical Clustering

Interactive Learning

Transductive SVM

Value iteration

Bayes' theorem

Perceptron

Maximum A Posteriori (MAP)

Geometric Margins

Sample Complexity

Structural Risk Minimization

Maximizing Data Likelihood

Sampling Bias

Reinforcement learning (RL)

10-401 Introduction to Machine Learning Carnegie Mellon University Spring 2018 A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.  Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as decision tree learning, Support Vector Machines, neural networks, boosting, statistical learning methods, unsupervised learning, active leaerning, and reinforcement learning. Short programming assignments include hands-on experiments with various learning algorithms.   - Machine Learning, Tom Mitchell. (optional)
- Pattern Recognition and Machine Learning, Christopher Bishop. (optional)
- Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, [available online](https://ebookcentral.proquest.com/lib/cm/detail.action?docID=3339490), (optional)

10-401 Introduction to Machine Learning