Искусственный интеллект и машинное обучение: для разработчиков
Для тех, кто хотел бы работать в сфере ИИ и автоматизации, и ищет толковую информацию по теме.
Онлайн-курсы от ColumbiaX
Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.
What you’ll learn
Supervised learning techniques for regression and classification
Unsupervised learning techniques for data modeling and analysis
Probabilistic versus non-probabilistic viewpoints
Optimization and inference algorithms for model learning
Skip Syllabus DescriptionWeek 1: maximum likelihood estimation, linear regression, least squares
Week 2: ridge regression, bias-variance, Bayes rule, maximum a posteriori inference
Week 3: Bayesian linear regression, sparsity, subset selection for linear regression
Week 4: nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron
Week 5: logistic regression, Laplace approximation, kernel methods, Gaussian processes
Week 6: maximum margin, support vector machines, trees, random forests, boosting
Week 7: clustering, k-means, EM algorithm, missing data
Week 8: mixtures of Gaussians, matrix factorization
Week 9: non-negative matrix factorization, latent factor models, PCA and variations
Week 10: Markov models, hidden Markov models
Week 11: continuous state-space models, association analysis
Week 12: model selection, next steps
Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.
What you’ll learn
Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence
Building intelligent agents (search, games, logic, constraint satisfaction problems)
Machine Learning algorithms
Applications of AI (Natural Language Processing, Robotics/Vision)
Solving real AI problems through programming with Python
Skip Syllabus DescriptionWeek 1: Introduction to AI, history of AI, course logistics
Week 2: Intelligent agents, uninformed search
Week 3: Heuristic search, A* algorithm
Week 4: Adversarial search, games
Week 5: Constraint Satisfaction Problems
Week 6: Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors
Week 7: Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning
Week 8: Markov decision processes and reinforcement learning
Week 9: Logical Agent, propositional logic and first order logic
Week 10: AI applications (NLP)
Week 11: AI applications (Vision/Robotics)
Week 12: Review and Conclusion
Сертификат об окончании:
Книги о ИИ и автоматизации
Automate the Boring Stuff with Python
Practical programming for total beginners. Written by Al Sweigart.
За наводку спасибо Альберту.
Список будут пополняться по мере нахождения качественных материалов.