In this repo, Some of the best and most recent courses.
Artificial Intelligence
- 6-034 MIT Artificial Intelligence
- Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
- CS 188 Introduction to Artificial Intelligence-2014
Machine Learning
- Stanford CS229: Machine Learning
- 6-s897-machine-learning-for-healthcare-spring-2019
- Making Friends with Machine Learning
- Applied Machine Learning
- Introduction to Machine Learning (Tübingen)
- Introduction to machine learning (Munich)
- Statistical Machine Learning (Tübingen)
- Probabilistic Machine Learning
- Machine vision fall 2020
Deep Learning
- Stanford CS230: Deep Learning (2018)
- Practical Deep Learning for Coders (2020)
- Introduction to Deep Learning (MIT)
- Deep Learning: CS 182
- Deep Unsupervised Learning
- NYU Deep Learning SP21
- Deep Learning (Tübingen)
- Full Stack Deep Learning
Natural Language Processing
- NLP Course (Hugging Face)
- CS224N: Natural Language Processing with Deep Learning
- CMU Neural Networks for NLP
- CS224U: Natural Language Understanding
- CMU Advanced NLP
- Multilingual NLP
- Advanced NLP
- Text Mining and Analytics
Computer Vision
- Deep Learning for Computer Vision
- Deep Learning for Computer Vision (DL4CV)
- Convolutional Neural Networks for Visual Recognition
Reinforcement Learning
- Deep Reinforcement Learning
- Reinforcement Learning Lecture Series (DeepMind)
- Reinforcement Learning (Polytechnique Montreal, Fall 2021)
- Foundations of Deep RL
Graph ML
- Machine Learning with Graphs (Stanford)
- AMMI Geometric Deep Learning Course (2021)
- CS520: Knowledge Graphs Seminar (Spring 2021)
Data Mining
Related to Artificial Intelligence
- Self-Driving Cars (Tübingen)
- Computer Architecture Fall 2018
- 6.858 Computer systems Security
- Computational Cognitive Neuroscience
To learn some of the basics of ML:
- Linear Regression and Gradient Descent
- Logistic Regression
- Naive Bayes
- SVMs
- Kernels
- Decision Trees
- Introduction to Neural Networks
- Debugging ML Models ...
A series of mini lectures covering various introductory topics in ML:
- Explainability in AI
- Classification vs. Regression
- Precession vs. Recall
- Statistical Significance
- Clustering and K-means
- Ensemble models ...
Covers the foundations of deep learning, how to build different neural networks(CNNs, RNNs, LSTMs, etc...), how to lead machine learning projects, and career advice for deep learning practitioners.
- Deep Learning Intuition
- Adversarial examples - GANs
- Full-cycle of a Deep Learning Project
- AI and Healthcare
- Deep Learning Strategy
- Interpretability of Neural Networks
- Career Advice and Reading Research Papers
- Deep Reinforcement Learning
🔗 Link to Course 🔗 Link to Materials
To learn some of the most widely used techniques in ML:
- Optimization and Calculus
- Overfitting and Underfitting
- Regularization
- Monte Carlo Estimation
- Maximum Likelihood Learning
- Nearest Neighbours ...
The course serves as a basic introduction to machine learning and covers key concepts in regression, classification, optimization, regularization, clustering, and dimensionality reduction.
- Linear regression
- Logistic regression
- Regularization
- Boosting
- Neural networks
- PCA
- Clustering ...
Covers many supervised machine learning concepts.
- Machine Learning Basics
- Supervised Regression and Classification
- Performance Evaluation
- Classification and Regression Trees (CART)
- Information Theory
- Linear and Nonlinear Support Vector Machine
- Gaussian Processes
- ...
🔗 Link to Course(YouTube links are embedded within the chapters)
The course covers the standard paradigms and algorithms in statistical machine learning.
- KNN
- Bayesian decision theory
- Convex optimization
- Linear and ridge regression
- Logistic regression
- SVM
- Random Forests
- Boosting
- PCA
- Clustering ...
After finishing this course you will know:
- How to train models that achieve state-of-the-art results
- How to turn your models into web applications, and deploy them
- Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models
- The latest deep learning techniques that really matter in practice
- How to implement stochastic gradient descent and a complete training loop from scratch
- How to think about the ethical implications of your work, to help ensure that you're making the world a better place and that your work isn't misused for harm ...
To learn some of the latest graph techniques in machine learning:
- PageRank
- Matrix Factorizing
- Node Embeddings
- Graph Neural Networks
- Knowledge Graphs
- Deep Generative Models for Graphs ...
To learn the probabilistic paradigm of ML:
- Reasoning about uncertainty
- Continuous Variables
- Sampling
- Markov Chain Monte Carlo
- Gaussian Distributions
- Graphical Models
- Tuning Inference Algorithms ...
To learn some of the fundamentals of deep learning:
- Introduction to Deep Learning
To learn some of the widely used techniques in deep learning:
- Machine Learning Basics
- Error Analysis
- Optimization
- Backpropagation
- Initialization
- Batch Normalization
- Style transfer
- Imitation Learning ...
To learn the latest and most widely used techniques in deep unsupervised learning:
- Autoregressive Models
- Flow Models
- Latent Variable Models
- Self-supervised learning
- Implicit Models
- Compression ...
To learn some of the advanced techniques in deep learning:
- Neural Nets: rotation and squashing
- Latent Variable Energy Based Models
- Unsupervised Learning
- Generative Adversarial Networks
- Autoencoders ...
This course introduces the practical and theoretical principles of deep neural networks.
- Computation graphs
- Activation functions and loss functions
- Training, regularization and data augmentation
- Basic and state-of-the-art deep neural network architectures including convolutional networks and graph neural networks
- Deep generative models such as auto-encoders, variational auto-encoders and generative adversarial networks ...
Learn about different NLP concepts and how to apply language models and Transformers to NLP:
- What is Transfer Learning?
- BPE Tokenization
- Batching inputs
- Fine-tuning models
- Text embeddings and semantic search
- Model evaluation
- ...
To learn the latest approaches for deep learning based NLP:
- Dependency parsing
- Language models and RNNs
- Question Answering
- Transformers and pretraining
- Natural Language Generation
- T5 and Large Language Models
- Future of NLP ...
To learn the latest neural network based techniques for NLP:
- Language Modeling
- Efficiency tricks
- Conditioned Generation
- Structured Prediction
- Model Interpretation
- Advanced Search Algorithms ...
To learn the latest concepts in natural language understanding:
- Grounded Language Understanding
- Relation Extraction
- Natural Language Inference (NLI)
- NLU and Neural Information Extraction
- Adversarial testing ...
To learn:
- Basics of modern NLP techniques
- Multi-task, Multi-domain, multi-lingual learning
- Prompting + Sequence-to-sequence pre-training
- Interpreting and Debugging NLP Models
- Learning from Knowledge-bases
- Adversarial learning ...
To learn the latest concepts for doing multilingual NLP:
- Typology
- Words, Part of Speech, and Morphology
- Advanced Text Classification
- Machine Translation
- Data Augmentation for MT
- Low Resource ASR
- Active Learning ...
To learn advanced concepts in NLP:
- Attention Mechanisms
- Transformers
- BERT
- Question Answering
- Model Distillation
- Vision + Language
- Ethics in NLP
- Commonsense Reasoning ...
To learn some of the fundamental concepts in CV:
- Introduction to deep learning for CV
- Image Classification
- Convolutional Networks
- Attention Networks
- Detection and Segmentation
- Generative Models ...
To learn modern methods for computer vision:
- CNNs
- Advanced PyTorch
- Understanding Neural Networks
- RNN, Attention and ViTs
- Generative Models
- GPU Fundamentals
- Self-Supervision
- Neural Rendering
- Efficient Architectures
To learn about concepts in geometric deep learning:
- Learning in High Dimensions
- Geometric Priors
- Grids
- Manifolds and Meshes
- Sequences and Time Warping ...
To learn the latest concepts in deep RL:
- Intro to RL
- RL algorithms
- Real-world sequential decision making
- Supervised learning of behaviors
- Deep imitation learning
- Cost functions and reward functions ...
The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence.
- Introduction to RL
- Dynamic Programming
- Model-free algorithms
- Deep reinforcement learning ...
To learn full-stack production deep learning:
- ML Projects
- Infrastructure and Tooling
- Experiment Managing
- Troubleshooting DNNs
- Data Management
- Data Labeling
- Monitoring ML Models
- Web deployment ...
Covers the fundamental concepts of deep learning
- Single-layer neural networks and gradient descent
- Multi-layer neural networks and backpropagation
- Convolutional neural networks for images
- Recurrent neural networks for text
- Autoencoders, variational autoencoders, and generative adversarial networks
- Encoder-decoder recurrent neural networks and transformers
- PyTorch code examples
🔗 Link to Course 🔗 Link to Materials
Covers the most dominant paradigms of self-driving cars: modular pipeline-based approaches as well as deep-learning based end-to-end driving techniques.
- Camera, lidar and radar-based perception
- Localization, navigation, path planning
- Vehicle modeling/control
- Deep Learning
- Imitation learning
- Reinforcement learning
Designing autonomous decision making systems is one of the longstanding goals of Artificial Intelligence. Such decision making systems, if realized, can have a big impact in machine learning for robotics, game playing, control, health care to name a few. This course introduces Reinforcement Learning as a general framework to design such autonomous decision making systems.
- Introduction to RL
- Multi-armed bandits
- Policy Gradient Methods
- Contextual Bandits
- Finite Markov Decision Process
- Dynamic Programming
- Policy Iteration, Value Iteration
- Monte Carlo Methods
- ...
🔗 Link to Course 🔗 Link to Materials
A mini 6-lecture series by Pieter Abbeel.
- MDPs, Exact Solution Methods, Max-ent RL
- Deep Q-Learning
- Policy Gradients and Advantage Estimation
- TRPO and PPO
- DDPG and SAC
- Model-based RL
Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning as a method to incorporate world knowledge.
- Knowledge Graph Definition
- Applications of Knowledge Graphs
- Knowledge Graphs for Data Integration in Enterprises
- Knowledge Graphs in Artificial Intelligence
- Knowledge Graphs as the output of Machine Learning
- ...
This course introduces students to representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Students learn applications of rule chaining, heuristic search, constrain propagation, constrained search, inheritance, and other problem-solving paradigms. They also learn applications of identification trees, neural nets, genetic algorithms, support-vector machines, boosting, and other learning paradigms.
- Search: depth-first, breadth-first, hill-climbing, beam, branch and bound, A*
- Constrains: Interpreting line drawings, Visuall object recognition, Domain reduction
- Nearest Neighbors
- Identification Trees
- Neural nets, Deep neural nets
- SVM, Model Merging, Cross-Modal Coupling
- ...
This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows.
- What makes Healthcare unique?
- Deep dive into clinical data
- Risk Stratification
- Natural Language Processing
- Application of ML to Cardiac Imaging
- ML for Pathology
- Causal inference
- Reinforcement Learning
- Disease Progressing Modeling and Subtyping
- Robustness to Dataset shift
- ...
introduction to the field of computer vision, discussing its history and key challenges. We emphasize that computer vision encompasses a wide variety of different tasks, and that despite the recent successes of deep learning we are still a long way from realizing the goal of human-level visual intelligence.
- Cambrian Explosion
- Camera Obscura
- Hubel and Wiesel
- Normalized Cut
- Face Detection
- Spatial Pyramid Matching
- Histogram of Oriented Gradients
- ImageNet Challenge
- PASCAL Visual Object Challenge
- ...
As an introductory course on data mining, this course introduces the concepts, algorithms, techniques, and systems of data warehousing and data mining, including (1) what is data mining? (2) get to know your data, (3) data preprocessing, integration and transformation, (4) design and implementation of data warehouse and OLAP systems, (5) data cube technology, (6&7) mining frequent patterns and assoication: basic concepts and advanced methods, (8&9) classification: basic concepts and advanced techniques, and (10) cluster analysis: basic concepts.
- Introduction
- Know Your Data
- Data Preprocessing
- Data Warehousing & OLAP
- Data Cube Technology
- Mining Frequent Patterns and Associations: Basic Concepts
- Classification: Basic Concepts
- Classification: Advanced Methods
- Cluster Analysis: Basic Concepts
Note: This course contains only Lecture notes without any Videos
This course focuses on machine vision. Topics include:
- Deriving a symbolic description of the environment from an image.
- Understanding physics of image formation.
- Image analysis as an inversion problem.
- Binary image processing and filtering of images as preprocessing steps.
- Recovering shape, lightness, orientation, and motion.
- Using constraints to reduce the ambiguity.
- Photometric stereo and extended Gaussian sphere.
- Applications to robotics; intelligent interaction of machines with their environment.
Note: This course contains only Lecture notes without any Videos
Computer Architecture has several goals:
- Grasp basic principles of combinational and sequential logic design.
- Have a high-level understanding of how to design a general-purpose computer, starting with simple logic gates.
- To provide you with a mental model of how high-level language programs get executed on computer hardware
- To introduce you to the organization and performance analysis of modern computers,
- Expose you to the hardware-level mechanisms for exposing parallelism.
Note: This course contains only Lecture notes without any Videos
Computer Security studies the design and implementation of secure computer systems. Lectures cover threat models, attacks that compromise security, and techniques for achieving security, based on recent research papers. Topics include operating system (OS) security, capabilities, information flow control, language security, network protocols, hardware security, and security in web applications.
- Introduction
- Control Hijacking Attacks
- Buffer Overflow Exploits and Defenses
- Privilege Separation
- Capabilities
- Sandboxing Web Applications
- ...
This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
- Overview Text Mining and Analytics
- Natural language content analysis
- Text Representation
- Word Association Mining and Analysis
- Paradigmatic Relation Discovery
- Syntagmatic Relation Discovery Entropy
- ...
introduction to the field of Computational Cognitive Neuroscience, where computer models of the brain are used to understand a wide range of cognitive functions, including perception, attention, motor control, learning, memory, language, and executive function.
- Introduction to Emergance
- Phenomena
- Detector model
- Pandemonium
- Dimensions
- Neurons in the dark
- ...
🔗 Link to the Book 🔗 Link to Course
Introduction to Artificial Intelligence by UC Berkeley.
- Introduction to AI
- Uninformed Search
- A* Search and Heuristics
- Markov Decision Processes
- Reinforcement Learning
- ...
What is this course about? What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.
- Linear Classifiers
- Features, Neural Networks
- Generalization, K-means
- Dynamic Programming, Uniform cost search
- Markov Descision Processes 1
- ...