Discover 9 Free AI Courses Provided by Stanford University
Artificial Intelligence AI is fundamentally changing various sectors fostering innovation and altering our daily and professional lives With the increasing need for AI proficiency educational institutions are making learning more accessible Stanford University a pioneer in AI research and education offers several free AI courses suitable for different expertise levels This article highlights nine complimentary AI courses from Stanford University that can help you build a robust understanding of this advanced field
1 Machine Learning CS229
Instructor Andrew Ng
Overview This highly popular and detailed course on machine learning is taught by the esteemed AI specialist Andrew Ng It covers a broad spectrum of topics such as supervised learning unsupervised learning and reinforcement learning along with practical applications and real-world case studies
Key Topics
- Linear regression
- Logistic regression
- Neural networks
- Support vector machines
- Anomaly detection
Platform Stanford Online2 Convolutional Neural Networks for Visual Recognition CS231n
Instructors Fei-Fei Li Justin Johnson Serena Yeung
Overview This course delves into deep learning techniques for computer vision It explores convolutional neural networks CNNs and their uses in image classification object detection and segmentation
Key Topics - Convolutional layers
- Pooling layers
- Transfer learning
- Object detection algorithms
Platform Stanford Online3 Natural Language Processing with Deep Learning CS224n
Instructors Christopher Manning Abigail See
Overview This course offers a comprehensive look at natural language processing NLP using deep learning methods Subjects include word embeddings sequence models and attention mechanisms
Key Topics - Word2Vec
- Recurrent neural networks RNNs
- Long short-term memory LSTM
- Transformer models
Platform Stanford Online4 Reinforcement Learning CS234
Instructor Emma Brunskill
Overview This course introduces the basics of reinforcement learning RL where agents learn to make decisions by interacting with their surroundings
Key Topics - Markov decision processes
- Q-learning
- Policy gradient methods
- Deep reinforcement learning
Platform Stanford Online5 Probabilistic Graphical Models CS228
Instructor Daphne Koller
Overview This course covers probabilistic graphical models for representing complex distributions in high-dimensional spaces It includes both theory and practical applications
Key Topics - Bayesian networks
- Markov networks
- Inference algorithms
- Learning algorithms
Platform Stanford Online6 Deep Learning for Natural Language Processing CS224d
Instructor Richard Socher
Overview This course zeroes in on deep learning techniques for NLP tasks covering various neural network architectures and their applications in language modeling translation and sentiment analysis
Key Topics - Recursive neural networks
- Sequence-to-sequence models
- Attention mechanisms
- Generative models
Platform Stanford Online7 Introduction to Robotics CS223A
Instructor Oussama Khatib
Overview This introductory course on robotics covers the fundamental principles and techniques used in robot design and control
Key Topics - Kinematics
- Dynamics
- Control systems
- Robot perception
Platform Stanford Online8 Computational Genomics CS273A
Instructor Anshul Kundaje
Overview This course examines the intersection of AI and genomics focusing on computational methods for genomic data analysis
Key Topics - DNA sequencing
- Gene expression analysis
- Genome-wide association studies
- Machine learning in genomics
Platform Stanford Online9 AI for Healthcare CS342
Instructor Nigam Shah
Overview This course explores the use of AI in healthcare covering topics such as medical imaging electronic health records and personalized medicine
