Large Language Model (LLM)

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

Principal Component Analysis (PCA)

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

N-gram Language Models

Text classification

Word Embeddings

Sequence Models

Constituency Parsing

Dependency Parsing

Recurrent neural network (RNN)

Machine Translation

Transformer (machine learning model)

Contextualized Representations and Pre-training

Question Answering (QA)

Long Short-Term Memory (LSTM)

COS 484: Natural Language Processing Princeton University Spring 2023 This course introduces the basics of NLP, including recent deep learning approaches. It covers a wide range of topics, such as language modeling, text classification, machine translation, and question answering. ### What is this course about?

Recent advances have ushered in exciting developments in natural language processing (NLP), resulting in systems that can translate text, answer questions and even hold spoken conversations with us. This course will introduce students to the basics of NLP, covering standard frameworks for dealing with natural language as well as algorithms and techniques to solve various NLP problems, including recent deep learning approaches. Topics covered include language modeling, representation learning, text classification, sequence tagging, syntactic parsing, machine translation, question answering and others.  - Required: [COS 324](https://www.cs.princeton.edu/courses/archive/fall21/cos324/), knowledge of probability, linear algebra, multivariate calculus.
- Proficiency in Python: programming assignments and projects will require use of Python, Numpy and PyTorch. ### Reading:

There is no required textbook for this class, and you should be able to learn everything from the lectures and assignments. However, if you would like to pursue more advanced topics or get another perspective on the same material, here are some books (all of them can be read free online):

- Dan Jurafsky and James H. Martin. [Speech and Language Processing (3rd ed. draft).](https://web.stanford.edu/~jurafsky/slp3/)
- Jacob Eisenstein. [Natural Language Processing ](https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf)
- Christopher Manning and Hinrich Schütze. [Foundations of Statistical Natural Language Processing](https://nlp.stanford.edu/fsnlp/).

COS 484: Natural Language Processing

Natural Language Processing

Natural Language Processing (NLP)

Task-Oriented Agents

Knowledge Base Query Agents

GenieScript

Architecture of Virtual Assistants

Natural Language Programming

Multimodal Virtual Assistants

Training Conversational Virtual Assistants

Automatic Speech Recognition

Multilingual Virtual Assistants

Text-based Question Answering

General Virtual Assistants

CS 224V Conversational Virtual Assistants with Deep Learning Stanford University Fall 2022 This course focuses on the creation of effective, personalized, conversational assistants using large language neural models. It involves both theory and practical assignments, offering students a chance to design their own open-ended course project. Familiarity with NLP and task-oriented agents is beneficial. Computers will transform into effective, personalized, conversational assistants for everybody, including the pre-literate and the non-literate. Commercial chatbots today are notoriously brittle as they are hardcoded to handle a few possible choices of user inputs. Recently introduced large language neural models, such as GPT-3, are remarkably fluent, but they are often erroneous and prone to hallucinations. This course studies how we can tame these neural models into robust, trustworthy, and cost-effective conversational agents across all industries and languages.

Students will learn both the theory and practice with **two assignments**, and a **major open-ended course project** of their own design.

Topics include:

- Formal, executable meaning representation for conversational virtual assistants
- Contextual neural semantic parser for translating dialogues into a formal representation.
- Automatic generation of training data for dialogues from high-level schema and API specification with large language models.
- Using large language models in virtual assistants.
- Multilingual, mixed-initiative, multimodal assistants.
- Federated privacy-protecting assistants.   

CS 224V Conversational Virtual Assistants with Deep Learning

Deep Learning