CMP761 - PROBABILISTIC LEARNING
Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
---|---|---|---|---|---|---|
PROBABILISTIC LEARNING | CMP761 | Any Semester/Year | 3 | 0 | 3 | 9 |
Prequisites | None | |||||
Course language | English | |||||
Course type | Elective | |||||
Mode of Delivery | Face-to-Face | |||||
Learning and teaching strategies | Lecture Preparing and/or Presenting Reports Problem Solving Project Design/Management | |||||
Instructor (s) | Asst. Prof. Dr . Burcu Can Buğlalılar | |||||
Course objective | To make the student have an understanding on the statistical machine learning methods. | |||||
Learning outcomes |
| |||||
Course Content | - Probability Concepts - Generative Models for Discrete Data - Bayesian Statistics - Frequentist Statistics - Mixture Models - Linear Regression - Hidden Markov Models - Sampling - Graphic Models | |||||
References | - Kevin Murphy Machine Learning: a probabilistic perspective - Michael Lavine, Introduction to Statistical Thought - Chris Bishop, Pattern Recognition and Machine Learning - Daphne Koller & Nir Friedman, Probabilistic Graphical Models - Hastie, Tibshirani, Friedman, Elements of Statistical Learning - David J.C. MacKay Information Theory, Inference, and Learning Algorithms |
Course outline weekly
Weeks | Topics |
---|---|
Week 1 | Introduction to Machine Learning |
Week 2 | Concepts about Probability and Statistics |
Week 3 | Generative Models for Discrete Data |
Week 4 | Gaussian Models |
Week 5 | Bayesian Statistics |
Week 6 | Frequentist Statistics |
Week 7 | Linear Regression |
Week 8 | Mixture Models |
Week 9 | Hidden Markov Models |
Week 10 | Kernels |
Week 11 | Sampling methods |
Week 12 | Graphical Models |
Week 13 | Non-parametric Bayesian Modelling |
Week 14 | Student presentations |
Week 15 | Final exam preparation |
Week 16 | Final exam |
Assesment methods
Course activities | Number | Percentage |
---|---|---|
Attendance | 0 | 0 |
Laboratory | 0 | 0 |
Application | 0 | 0 |
Field activities | 0 | 0 |
Specific practical training | 0 | 0 |
Assignments | 0 | 0 |
Presentation | 0 | 0 |
Project | 1 | 50 |
Seminar | 0 | 0 |
Midterms | 0 | 0 |
Final exam | 1 | 50 |
Total | 100 | |
Percentage of semester activities contributing grade succes | 0 | 50 |
Percentage of final exam contributing grade succes | 0 | 50 |
Total | 100 |
WORKLOAD AND ECTS CALCULATION
Activities | Number | Duration (hour) | Total Work Load |
---|---|---|---|
Course Duration (x14) | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Specific practical training | 0 | 0 | 0 |
Field activities | 0 | 0 | 0 |
Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 8 | 112 |
Presentation / Seminar Preparation | 0 | 0 | 0 |
Project | 1 | 70 | 70 |
Homework assignment | 0 | 0 | 0 |
Midterms (Study duration) | 0 | 0 | 0 |
Final Exam (Study duration) | 1 | 40 | 40 |
Total Workload | 30 | 121 | 264 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
D.9. Key Learning Outcomes | Contrubition level* | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
1. Graduates should have a mastery of computer science as described by the core of the Body of Knowledge. | X | ||||
2. Graduates need understanding of a number of recurring themes, such as abstraction, complexity, and evolutionary change, and a set of general principles, such as sharing a common resource, security, and concurrency. | X | ||||
3. Graduates of a computer science program need to understand how theory and practice influence each other. | X | ||||
4. Graduates need to think at multiple levels of detail and abstraction. | X | ||||
5. Students will be able to think critically, creatively and identify problems in their research. | X | ||||
6. Graduates should have been involved in at least one substantial project. | X | ||||
7. Graduates should realize that the computing field advances at a rapid pace. | X | ||||
8. Graduates should conduct research in an ethical and responsible manner. | X | ||||
9. Graduates should have good command of technical terms in both Turkish and English. | X | ||||
10. Graduates should understand the full range of opportunities available in computing. | X | ||||
11. Graduates should understand that computing interacts with many different domains. | X | ||||
12. Graduates should develop the knowledge acquired at master level and apply scientific methods in order to solve scientific problems. | X | ||||
13. Graduates should develop a complete plan of a course in computer science and teach. | X |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest