Discover 9 Free AI Courses from Stanford University
Artificial Intelligence AI is reshaping various sectors sparking innovation and altering our daily lives and work environments As the demand for AI knowledge grows educational institutions are providing more accessible learning opportunities Stanford University a pioneer in AI research and education offers a selection of free AI courses for learners at different expertise levels This article highlights nine free AI courses from Stanford University that can help you build a strong foundation in this advanced field
1 Machine Learning CS229
Instructor Andrew Ng
Description This renowned and thorough course on machine learning is taught by the esteemed AI authority Andrew Ng It encompasses a wide spectrum of subjects such as supervised learning unsupervised learning and reinforcement learning along with practical applications and real-world case studies
Main 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
Description This course is centered on deep learning techniques for computer vision covering convolutional neural networks CNNs and their use in image classification object detection and segmentation
Main 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
Description This course offers a comprehensive understanding of natural language processing NLP utilizing deep learning methods Topics include word embeddings sequence models and attention mechanisms
Main Topics - Word2Vec
- Recurrent neural networks RNNs
- Long short-term memory LSTM
- Transformer models
Platform Stanford Online4 Reinforcement Learning CS234
Instructor Emma Brunskill
Description This course introduces the basics of reinforcement learning RL a form of machine learning where agents learn to make decisions by interacting with their surroundings
Main Topics - Markov decision processes
- Q-learning
- Policy gradient methods
- Deep reinforcement learning
Platform Stanford Online5 Probabilistic Graphical Models CS228
Instructor Daphne Koller
Description This course delves into probabilistic graphical models which are used to represent intricate distributions over high-dimensional spaces It covers both theoretical principles and practical applications
Main Topics - Bayesian networks
- Markov networks
- Inference algorithms
- Learning algorithms
Platform Stanford Online6 Deep Learning for Natural Language Processing CS224d
Instructor Richard Socher
Description This course focuses on deep learning techniques tailored for NLP tasks It discusses various neural network architectures and their applications in language modeling translation and sentiment analysis
Main Topics - Recursive neural networks
- Sequence-to-sequence models
- Attention mechanisms
- Generative models
Platform Stanford Online7 Introduction to Robotics CS223A
Instructor Oussama Khatib
Description This introductory course on robotics covers the essential principles and methods used in robot design and control
Main Topics - Kinematics
- Dynamics
- Control systems
- Robot perception
Platform Stanford Online8 Computational Genomics CS273A
Instructor Anshul Kundaje
Description This course investigates the intersection of AI and genomics emphasizing computational methods for genomic data analysis
Main Topics - DNA sequencing
- Gene expression analysis
- Genome-wide association studies
- Machine learning in genomics
Platform Stanford Online9 AI for Healthcare CS342
Instructor Nigam Shah
Description This course explores the application of AI in healthcare discussing topics such as medical imaging electronic health records and personalized medicine
Source Link Zephyrnet
