AI courses provide students and professionals looking to expand their abilities in various aspects of artificial intelligence with beginner-level to advanced-level training programs that cost a set course fee. To know more, check out Étudier l’IA à l’école spécialisée
This series of Artificial Intelligence courses has been tailored from Stanford’s on-campus graduate curriculum, providing working professionals with an opportunity to explore AI topics at graduate-level depth. Each 10-week course includes lectures and coding assignments.
Basics of AI
Artificial Intelligence (AI) refers to the simulation of human intelligence by computers, robots, and software designed to mimic it. AI allows computers, robots, and software to perform tasks that would normally require human intelligence, such as learning, reasoning, problem-solving, and perception.
AI technology has existed since the 1950s, yet its presence is becoming more visible in everyday life thanks to self-driving cars, chatbots such as ChatGPT or Alexa, image recognition systems, and other forms of artificial intelligence (AI). Business leaders must become acquainted with AI’s basic concepts to fully comprehend its impactful potential within their organization and on society at large.
Machine learning (ML) is one of the most prevalent types of AI, allowing computers to adapt and learn over time without being explicitly programmed for it. ML uses large datasets to teach AI systems how to perform specific tasks, such as classifying images of circles and squares by showing many examples and measuring accuracy responses; then, those models with more accurate responses are used as decision-makers in future instances to avoid making mistakes in future cases, known as reinforcement learning.
Machine Learning
Machine learning (ML) is an area of artificial intelligence (AI) in which machines learn by recognizing patterns and performing tasks without being explicitly programmed. ML can automate and optimize business processes while hastening data discovery and application development.
Artificial intelligence systems such as Google Translate and self-driving cars rely heavily on natural language processing technology for familiar technologies like chatbots and digital assistants, voice recognition software, computer vision techniques to detect images, as well as providing voice recognition software and image classification features that power image detection/classification software – which in turn power headsets such as Google Glasses or Apple ARs.
Machine learning algorithms are being applied in every aspect of our lives today – from pattern recognition in e-commerce and social media to fraud prediction, traffic forecasts, email filtering, agriculture advice, and medical diagnosis. A recent Work of the Future brief highlighted that no occupation will remain immune from machine learning’s influence; rather it will replace some manual tasks, improve existing processes, and enable us to do things we couldn’t before4.4
Deep Learning
Machine learning and AI help businesses automate functions and turn data into insights more effectively. However, to remain competitive, they require a hybrid AI architecture capable of supporting all the various forms of data found across mainframes, data centers, public/private clouds, and edge devices.
Deep learning is one of the cornerstones of an effective AI platform. It goes beyond machine learning with large neural networks that mimic human decision-making power and provide decision support that goes far beyond user input. By identifying patterns and relationships, recognizing possibilities, and evaluating outcomes, deep learning models can make complex decisions without human input.
These powerful algorithms form the backbone of many of the technologies we rely on today, such as chatbots and virtual assistants like Siri and Alexa. Furthermore, these powerful algorithms underlie natural language processing – which enables computers to understand human languages – as well as computer vision technology that captures images or video and interprets its surroundings – and even generative AI, which uses powerful base models to analyze unlabeled data before developing its own feature set for an individual task.
Neural Networks
Explore neural networks as they relate to AI and how you can apply them in your work. This module covers key concepts and ideas while giving you hands-on skills with tools like GitHub Copilot and ChatGPT. Furthermore, this unit introduces you to large language models and text-based generative AI.
Gain a comprehensive overview of the mathematics underlying not only basic AI models and algorithms but also more recently developed technologies like transformers and graph neural nets. Learn how to overcome power, memory, and processing constraints associated with IoT devices such as wearables when deploying these neural networks.
Get ahead in AI by mastering its core technologies. Stanford Online’s specialization – comprised of 10 courses – allows you to explore AI topics at graduate-level depth with flexible scheduling that fits seamlessly with working professionals’ lives. The course content has been adapted from Stanford’s on-campus graduate courses, while live facilitators are available via 1-on-1 calls, group office hours, or Slack to assist as needed. Taking two hours per week as a coursework timeframe, the specialization can take three months to complete if committed.
Ethics of AI
As AI becomes more widespread, ethical considerations regarding its development and usage have arisen. At San Francisco State University’s Certificate Program for Ethics of AI, students learn the fundamental concepts and theories in this field while having opportunities to apply their knowledge to real-world applications that directly affect people.
Learn the effects of AI systems design and implementation on human lives, including issues like privacy, security, fairness, transparency, and trustworthiness. You’ll also explore ethical AI policies and practices.
As leading companies attempt to develop AI that consumers trust, they must also establish guidelines for their research and product creation to prevent ethical violations. Through this certificate program at SFSU, SFSU’s expert faculty and graduate students will work on creating these guidelines while supporting community members in better understanding AI.
