CS221: Artificial Intelligence: Principles and Techniques

0 - Overview Artificial Intelligence Course

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1 - Linear Classifiers, SGD

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2 - Features, Neural Networks

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3 - Generalization, K-means

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Search 1: Dynamic Programming, Uniform Cost Search

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Search 2: A*

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Markov Decision Processes 1 - Value Iteration

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Markov Decision Processes 2 - Reinforcement Learning

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Game Playing 1 - Minimax, Alpha-beta Pruning

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Game Playing 2 - TD Learning, Game Theory

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Factor Graphs 1 - Constraint Satisfaction Problems

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Factor Graphs 2 - Conditional Independence

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Bayesian Networks 1 - Inference

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Bayesian Networks 2 - Forward-Backward

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Bayesian Networks 3 - Maximum Likelihood

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Logic 1 - Propositional Logic

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Logic 2 - First-order Logic

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Deep Learning

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Conclusion

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CS229: Machine Learning

1 - Introduction

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2 - Supervised learning setup, LMS

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3 - Stanford CS229 I Weighted Least Squares, Logistic regression, Newton's Method

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4 - Exponential family, Generalized Linear Models

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5 - Gaussian discriminant analysis, Naive Bayes

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6 - Naive Bayes, Laplace Smoothing

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7 - Kernels

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8 - Neural Networks 1

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9 - Neural Networks 2 (backprop)

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10 - Bias - Variance, Regularization

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11 - Feature / Model selection, ML Advice

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12 - Stanford CS229 I K-Means, GMM (non EM), Expectation Maximization

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13 - GMM (EM)

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14 - Factor Analysis/PCA

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15 - PCA/ICA

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16 - Self-supervised learning

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17 - Stanford CS229 I Basic concepts in RL, Value iteration, Policy iteration

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18 - Stanford CS229 I Societal impact of ML (Guest by Prof. James Zou)

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20 - Model-based RL, Value function approximator

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CS230: Deep Learning

1 - Class Introduction & Logistics, Andrew Ng

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2 - Deep Learning Intuition

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3 - Full-Cycle Deep Learning Projects

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4 - Adversarial Attacks / GANs

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5 - AI + Healthcare

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6 - Deep Learning Project Strategy

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7 - Interpretability of Neural Network

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8 - Career Advice / Reading Research Papers

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9 - Deep Reinforcement Learning

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10 - Chatbots / Closing Remarks

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CS224N: Natural Language Processing with Deep Learning

1 - Introduction and Word Vectors

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2 - Word Vectors and Word Senses

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3 - Neural Networks

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4 - Backpropagation

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5 - Dependency Parsing

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6 - Language Models and RNNs

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7 - Vanishing Gradients, Fancy RNNs

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8 - Translation, Seq2Seq, Attention

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9 - Practical Tips for Projects

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10 - Question Answering

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11 - Convolutional Networks for NLP

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12 - Subword Models

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13 - Contextual Word Embeddings

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14 - Transformers and Self-Attention

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15 - Natural Language Generation

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16 - Coreference Resolution

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17 - Multitask Learning

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18 - Constituency Parsing, TreeRNNs

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19 - Bias in AI

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20 - Future of NLP + Deep Learning

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Low Resource Machine Translation

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BERT and Other Pre-trained Language Models

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CS231N: Convolutional Neural Networks for Visual Recognition

1 - Introduction to Convolutional Neural Networks for Visual Recognition

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2 - Image Classification

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3 - Loss Functions and Optimization

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4 - Introduction to Neural Networks

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5 - Convolutional Neural Networks

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6 - Training Neural Networks I

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7 - Training Neural Networks II

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8 - Deep Learning Software

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9 - CNN Architectures

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10 - Recurrent Neural Networks

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11 - Detection and Segmentation

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12 - Visualizing and Understanding

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13 - Generative Models

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14 - Deep Reinforcement Learning

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15 - Efficient Methods and Hardware for Deep Learning

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16 - Adversarial Examples and Adversarial Training

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CS234: Reinforcement Learning

1 - Introduction to Reinforcement Learning

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2 - Tabular MDP Planning

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3 - Policy Evaluation

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4 - Q learning and Function Approximation

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5 - Policy Search 1

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6 - Policy Search 2

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7 - Policy Search 3

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8 - Offline RL 1

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Guest on DPO: Rafael Rafailov, Archit Sharma, Eric Mitchell I

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10 - Offline RL 3

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11 - Exploration 1

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12 - Exploration 2

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13 - Exploration 3

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14 - Multi-Agent Game Playing

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15 - Emma Brunskill & Dan Webber

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16 - Value Alignment

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CS236: Deep Generative Models

1 - Introduction

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2 - Background

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3 - Autoregressive Models

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4 - Maximum Likelihood Learning

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5 - VAEs

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6 - VAEs

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7 - Normalizing Flows

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8 - GANs

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9 - Normalizing Flows

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10 - GANs

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11 - Energy Based Models

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12 - Energy Based Models

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13 - Score Based Models

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14 - Energy Based Models

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15 - Evaluation of Generative Models

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16 - Score Based Diffusion Models

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17 - Discrete Latent Variable Models

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18 - Diffusion Models for Discrete Data

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CS106A: Programming Methodology

1 - Programming Methodology

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2 - Programming Methodology

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3 - Programming Methodology

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