Prediction is the heart of scientific discipline, and generalization from data is the basis of machine learning and statistics. Fields aim at the field are, the prediction from data. It provides a selection of the important topics from both of these subjects. Machine learning and statistical methods are used the scientific world for their use in handling the “information overload” that characterizes our current digital age.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
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Course Credit: MIT
Course Curriculum
| Rule mining and the Apriori algorithm | 00:15:00 | ||
| R for machine learning | 00:16:00 | ||
| Fundamentals of learning | 00:15:00 | ||
| Inference | 00:14:00 | ||
| Clustering | 00:13:00 | ||
| k-nearest neighbors | 00:15:00 | ||
| Naïve Bayes | 00:14:00 | ||
| Decision trees | 00:14:00 | ||
| Logistic regression | 00:14:00 | ||
| Boosting | 00:13:00 | ||
| Convex optimization | 00:13:00 | ||
| Support vector machines | 00:16:00 | ||
| Kernels | 00:15:00 | ||
| Statistical learning theory | 00:14:00 | ||
| Bayesian analysis | 00:12:00 | ||
| Assessment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
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