The excellent and popular Machine Learning class from Coursera and Andrew Ng starts today. This is the 3rd or 4th run of the course.
In about 1 month, the course, Introduction to Recommender Systems, will begin on Coursera. The course is being offered by the Computer Science and Engineering Department from the University of Minnesota.
The course is 14 weeks long and has 2 tracks:
- Programming Track – 6 different recommender systems will be programmed
- Concept Track – great for people that want to know about recommender systems, but don’t want program
Recommender systems are an important part of data science, and this course looks to provide an excellent in-depth overview of the topic.
Machine Learning is a term that can mean different things to different people. Andrew Ng, cofounder of Coursera and Professor at Stanford, provides two definitions in his popular Machine Learning Course. The first definition comes from Arthur Samuel around 1959.
Field of study that gives computers the ability to learn without being explicitly programmed.
The second definition comes from Tom Mitchell’s 1997 Machine Learning textbook. This definition is a bit more formal and rigorous. This book defines a well-posed learning problem as:
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
Machine Learning Categories
Machine learning can be broken down into a few categories. The two most popular are supervised and unsupervised learning. A couple other categories are recommender systems and reinforcement learning.
Probably the most common category of machine learning, supervised learning is concerned with fitting a model to labeled data. Labeled data is data that has the correct answer supplied. Regression and Classification are the most common types of problems in supervised learning.
Unsupervised learning deals with unlabeled data. Therefore, the goal of unsupervised learning is to find structure in the data. Clustering is probably the most common technique.
Recommender systems deal with making recommendations based upon previously collected data. Reinforcement learning is concerned with maximizing the reward of a given agent(person, business, etc).
Most of the above information comes from the Coursera Machine Learning Course. There is still time to sign up since the first assignments are not due until the end of the week.
Andrew Ng’s wonderful Coursera course on machine learning starts today. It is not too late to sign up.
The list is ordered according to the level of difficulty.
- Descriptive just describe the data, common for census type of data
- Exploratory find relationships that were not clear beforehand, useful for defining future studies, remember correlation does not imply causation
- Inferential use a small dataset to say something about a larger population, most common goal of statistical analysis
- Predictive use data from some object to predict something(values) for another object, important to measure the right values and to use as much data as possible
- Causal what happens to one variable when you force another variable to change, usually requires a randomized study, this is the gold standard of data analysis
- Mechanistic understanding the exact changes in variables that lead to changes in other variables for individual objects, typically from engineering and physical sciences, data analysis can be used to infer the parameters if the equations are known
This list comes from information presented in the first week of the Coursera Data Analysis class.
The Coursera Data Analysis course started yesterday. This course would be an excellent follow-up to the Computing with Data Analysis course. For a bit more about the course, check out this video explaining the content. The course consists of lectures, quizzes, and some data analysis assignments. There is still plenty of time to signup and start analyzing.
Jeff Leak, instructor of the upcoming Coursera Data Analysis course, wrote up a nice blog post, The Landscape of Data Analysis, explaining the topics to be covered in the course. The topics look good. He also made a video explaining how data science fits in with other disciplines such as: computer science, medicine, statistics, and so on. The video is short (less than 5 minutes), so it is definitely worth the time.