Columbia University is offering free online course on Machine Learning. It is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.
In this course applicants will master the essentials of machine learning and algorithms to help improve learning from data without human intervention. The course will start on January 29, 2018.
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( votes)Course At A Glance
Length: 12 weeks
Effort: 8-10 hours per week
Subject: Computer Science
Institution: Columbia University and edX
Languages: English
Price: Free
Certificate Available: Yes. Verified Certificate for $300
Session: Course starts on January 29, 2018
Providers’ Details
Columbia University is one of the world’s most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis.
About This Course
Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.
Learning Outcomes
- 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
Instructors
Professor John W. Paisley
John Paisley is an Assistant Professor in the Department of Electrical Engineering at Columbia University. John is also an affiliated member of the Data Science Institute at Columbia.
Course Syllabus
Week 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
Requirements
- Calculus
- Linear algebra
- Probability and statistical concepts
- Coding and comfort with data manipulation
How To Join This Course
- Go to the course website link
- Create an edX account to SignUp
- Choose “Register Now” to get started.
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