What AI topics are covered in CS50's Introduction to Artificial Intelligence with Python?

The course covers search, knowledge-based agents, probability and uncertainty, optimization, machine learning, neural networks, image convolution, and natural language processing.

How do local search algorithms work in artificial intelligence?

Local search algorithms improve a current candidate solution by repeatedly moving to a neighboring state, often using limited memory.

What is the purpose of convolution kernels in neural networks?

A convolution kernel scans image pixels to detect features such as edges, patterns, and textures for image-recognition tasks.

Free Course Image Artificial intelligence

Free online course Artificial intelligence

Duration of the online course: 12 hours and 40 minutes

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Build real AI skills fast with a free online course: search, machine learning, neural nets and NLP, plus hands-on practice and a certificate option.

In this free course, learn about

Core AI concepts and types of problems AI systems solve
Search algorithms: BFS/DFS, A*, heuristics, and adversarial (minimax) search
Modeling knowledge with logic: sentences, inference, and knowledge-based agents
Probability foundations: conditional probability, Bayes' rule, and independence
Reasoning under uncertainty using Bayesian networks and probabilistic inference
Optimization methods and local search traits (hill climbing, simulated annealing)
Supervised learning basics: training from labeled data to generalize to new inputs
Common ML models: k-NN, perceptron, SVM, decision trees, and evaluation concepts
Neural networks: backpropagation, gradient descent, and modern deep learning ideas
Convolution for vision: kernel matrices/filters and feature extraction in images
Natural language processing tasks: parsing, sentiment, translation, and language modeling
Using Python to implement AI techniques and solve practical exercises/projects

About the free online course

Artificial intelligence is no longer a distant concept reserved for research labs; it powers recommendations, language tools, image recognition, and decision-making systems used every day. This free online course is designed to help you understand how AI actually works under the hood, connecting core theory with practical thinking you can apply to real problems. If you want to move from curiosity to capability, you will develop a solid foundation in the main approaches that drive modern intelligent systems.

You will start by learning what AI is and how to frame problems in a way machines can solve. From there, you will explore how an agent can search for solutions, represent knowledge about the world, and make decisions when information is incomplete or uncertain. These ideas form the backbone of many real products, where computers must choose actions efficiently, balance trade-offs, and update beliefs based on evidence.

As you progress, the course shifts toward the methods that allow machines to improve with experience. You will grasp the intuition behind optimization and why it is central to training effective models. You will also dive into machine learning, focusing on supervised learning as a practical pathway to building predictive systems. Along the way, you will strengthen your reasoning with targeted exercises that reinforce key concepts in probability, local search behavior, and model evaluation.

You will then step into neural networks and the fundamentals that make them so powerful, including the building blocks used in vision tasks such as convolution. Finally, you will learn how AI approaches language, tackling goals and techniques within natural language processing that aim to help machines interpret and generate human communication. By the end, you will be able to speak confidently about the major pillars of AI and approach new tools and frameworks with clarity, whether you are preparing for a tech role, enhancing your programming portfolio, or simply building future-proof skills.

Course content

Video class: Introduction - CS50's Introduction to Artificial Intelligence with Python 2020 01m
Video class: Search - Lecture 0 - CS50's Introduction to Artificial Intelligence with Python 2020 1h49m
Exercise: What is artificial intelligence?
Video class: Knowledge - Lecture 1 - CS50's Introduction to Artificial Intelligence with Python 2020 1h47m
Exercise: What are knowledge-based agents in artificial intelligence?

Video class: Uncertainty - Lecture 2 - CS50's Introduction to Artificial Intelligence with Python 2020 1h54m
Exercise: What is an important rule in probability theory?
Video class: Optimization - Lecture 3 - CS50's Introduction to Artificial Intelligence with Python 2020 1h44m
Exercise: What is a key characteristic of local search algorithms?

Video class: Learning - Lecture 4 - CS50's Introduction to Artificial Intelligence with Python 2020 1h45m
Exercise: What is the main idea behind supervised machine learning?
Video class: Neural Networks - Lecture 5 - CS50's Introduction to Artificial Intelligence with Python 2020 1h41m
Exercise: What is the role of a kernel matrix in image convolution?

Video class: Language - Lecture 6 - CS50's Introduction to Artificial Intelligence with Python 2020 1h54m
Exercise: What is a significant task within natural language processing aimed at making AI understand human languages?

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