You must be logged in to take this course → LOGIN | REGISTER NOW
The [course_title] course covers the fundamental theories of human cognition. Formal models, classical and contemporary artificial intelligence will be covered in the course. You will know the basic issues in human knowledge representation, inductive learning and reasoning. The discussion will be on the forms that our knowledge of the world takes, the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data, the data that are available to human learners and the innate knowledge of human being.
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 needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.
Having an Official Edukite Certification is a great way to celebrate and share your success. You can:
- Add the certificate to your CV or resume and brighten up your career
- Show it to prove your success
Course Credit: MIT
Course Curriculum
| Lct01 Introduction | 01:05:00 | ||
| Lct02 Foundations of Inductive Learning | 00:55:00 | ||
| Lct03 Knowledge Representation Spaces, Trees, Features | 00:50:00 | ||
| Lct04 Knowledge Representation Language and Logic 1 | 00:35:00 | ||
| Lct05 Knowledge Representation Language and Logic 2 | 00:35:00 | ||
| Lct06 Knowledge Representation Great Debates 1 | 00:20:00 | ||
| Lct07 Knowledge Representation Great Debates 2 | 00:30:00 | ||
| Lct08 Basic Bayesian Inference | 00:35:00 | ||
| Lct09 Graphical Models and Bayes Nets | 00:45:00 | ||
| Lct10 Simple Bayesian Learning 1 | 00:25:00 | ||
| Lct11 Simple Bayesian Learning 2 | 00:45:00 | ||
| Lct12 Probabilistic Models for Concept Learning and Categorization 1 | 00:35:00 | ||
| Lct13 Probabilistic Models for Concept Learning and Categorization 2 | 00:30:00 | ||
| Lct14 Unsupervised and Semi-supervised Learning | 00:20:00 | ||
| Lct15 Non-parametric Classification Exemplar Models and Neural Networks 1 | 00:25:00 | ||
| Lct16 Non-parametric Classification Exemplar Models and Neural Networks 2 | 00:20:00 | ||
| Lct17 Controlling Complexity and Occam’s Razor 1 | 00:20:00 | ||
| Lct18 Controlling Complexity and Occam’s Razor 2 | 00:10:00 | ||
| Lct19 Intuitive Biology and the Role of Theories | 00:25:00 | ||
| Lct20 Learning Domain Structures 1 | 00:35:00 | ||
| Lct21 Learning Domain Structures 2 | 00:20:00 | ||
| Lct22 Causal Learning | 00:35:00 | ||
| Lct23 Causal Theories 1 | 00:25:00 | ||
| Lct24 Causal Theories 2 | 00:35:00 | ||
| Assessment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
Course Reviews
No Reviews found for this course.
Related Courses
