In-context Learning

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)

Multi-class Classification

Python

Backpropagation

K-means clustering

Principal Component Analysis (PCA)

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

Meta-learning

Multi-task Learning

PyTorch

Fine-tuning

Black-box Meta-learning

Optimization-based Meta-learning

Non-Parametric Few-Shot Learning

Unsupervised pre-training

Task Construction

Large-scale Meta-optimization

Variational Inference

Bayesian Meta-learning

Domain Adaptation

Domain Generalization

Lifelong Learning

Transfer learning

CS 330 Deep Multi-Task and Meta Learning Stanford University Fall 2022 This course emphasizes leveraging shared structures in multiple tasks to enhance learning efficiency in deep learning. It provides a thorough understanding of multi-task and meta-learning algorithms with a focus on topics like self-supervised pre-training, few-shot learning, and lifelong learning. Prerequisites include an introductory machine learning course. The course is designed for graduate-level students. While deep learning has achieved remarkable success in many problems such as image classification, natural language processing, and speech recognition, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes:

- self-supervised pre-training for downstream few-shot learning and transfer learning
- meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly
- curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer

This is a graduate-level course. By the end of the course, students will be able to understand and implement the state-of-the-art multi-task learning and meta-learning algorithms and be ready to conduct research on these topics.  CS 229 or an equivalent introductory machine learning course is required. 

CS 330 Deep Multi-Task and Meta Learning

Deep Learning