About the content
Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?
In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.
Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.
Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.
All programming examples and assignments will be in Python, using Jupyter notebooks.
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
Professor of Computer Science and Engineering
UC San Diego
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I have a few things to say about this course. Firstly, the lectures content is very good as the main concepts of ML models are explained well by Sanjoy Dasgupta. However, he misses on explaining the F1-score, precision and recall metrics which are often used in solving practical problems. Secondly, the course does not teach any python code which is fair enough. however, it asks to do the programming assignments in python and these are quite challenging with no solutions provided. One of them for week 7 even asks in a form of a quiz question 'Were you able to correctly implement the kernel Perceptron algorithm?' giving the options 'of yes', 'almost' and 'no, I had a lot of trouble with this'. Unfortunately if you've chosen the third option, there is no feedback to help you. What makes it worse that the course staff do not monitor the discussion forum and those stuck with these assignments are unable to get help with their questions. There is no way to contact the course staff, as opposed to other courses on Edx which display the teaching university contact information on the course introduction page. I emailed Edx about this problem and they could not help much either. To sum up, this course is brilliant for auditing (i.e. watching the lecture videos) but becoming a verified learner is not worth it due to lack of the staff's support for this course.