Comprehensive course analysis
About the course
Artificial Intelligence has emerged as an increasingly impactful discipline in science and technology. AI applications are embedded in the infrastructure of many products and industries search engines, medical diagnoses, speech recognition, robot control, web search, advertising and even toys.
This professional course provides a broad overview of modern artificial intelligence. Learn how machines can engage in problem solving, reasoning, learning, and interaction. Design, test and implement algorithms. Gain an appreciation of this dynamic field.
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.
What you will learn
- Search (tree search, dynamic programming, uniform cost search)
- Constraint satisfaction problems (backtracking search, dynamic ordering, local search)
- Markov decision processes (policy evaluation, reinforcement learning, function approximation)
- Planning and game playing (evaluation functions, TD learning, Game theory)
- Machine learning (linear classification, loss minimization, neural networks, unsupervised learning)
- Bayesan networks
- Graphical models
- Logic (syntax versus semantics, first-order logic)
- Proficiency in Python: All coding assignments will be written in Python. You should be familiar with numpy and matplotlib, as well as basic shell commands (ssh, scp, ls, cd, rm, mv, cp, zip, etc.).
- Calculus and Linear Algebra: You should understand the following concepts from multivariable calculus and linear algebra: chain rule, gradients, matrix multiplication, matrix inverse.
- Probability: You should be familiar with basic probability distributions and be able to define the following concepts for both continuous and discrete random variables: Expectation, independence, probability distribution functions, and cumulative distribution functions.
- Basic CS Theory: This course assumes basic understanding of tree search, graph search, and greedy algorithms, as well as big-O notation. Those unfamiliar may enroll but must be prepared for additional self-study.
This course features classroom videos and assignments adapted from the CS221 graduate course delivered on-campus at Stanford. The content and workload have been modified to better suit working professionals. The course features:
- Classroom lecture videos edited and segmented to focus on essential content
- Problem sets enhanced with additional supports and scaffolding
- Office hours and support from Stanford-affiliated Course Assistants
- Cohort group connected via a vibrant Slack community, providing opportunities to network and collaborate with motivated learners from diverse locations and professional backgrounds
Fields: machine learning, natural language processing. Topics: unsupervised learning, structured prediction, statistical learning theory, grounded language acquisition, compositional semantics, program induction. Learning semantics: Natural language allows us to express complex ideas using a fe...
Dorsa Sadigh is an assistant professor in computer science and electrical engineering at Stanford University. Her research interests lie in the intersection of robotics, learning and control theory, and algorithmic human-robot interaction. Specifically, she works on developing efficient algorith...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.