Target group
- The course can be taken at any stage of studies.
- Suitable for non-computer science students.
- Taking the "Elements of AI: Introduction to AI" (Part I of the series) course is recommended but not required.
- The course may be offered among the university’s continuous learning courses.
Completion methods
- The course if completed online (fully distance learning) and there are no attendance requirements.
- The course is completed by doing exercises. Doing at least 90% of the exercises with minimum 50% correctness is required to successfully complete the course. All the exercises are automatically graded. There is no exam.
Open University and degree programme completion methods may be different.
Contents
Chapter 1: About this Course
Chapter 2: Optimization
- Brute-force search
- Hill climbing
- Games
Chapter 3: Dealing with uncertainty
- Probability fundamentals
- The Bayes Rule
- Naive Bayes classifier
Chapter 4: Machine learning
- Linear regression
- The nearest neighbor method
- Working with text
- Overfitting and cross validation
Chapter 5: Neural networks
- Logistic regression
- Neural networks
- Deep learning
Chapter 6: Conclusions
Assessment practices and criteria
- Pass / Fail grading.
- Successful completion requires 90% completed exercises and minimum 50% correctness.
- Only one attempt is allowed in the exercises.
- If the student fails to achieve 50% correctness, they can start again.