CNG562 MACHINE LEARNING
| Course Code: | 3550562 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| Department: | Computer Engineering |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | |
| Offered Semester: | Fall or Spring Semesters. |
Course Objectives
After finishing the course, student should demonstrate the following skills:
1) Ability to differentiate between different learning models, and perfrome proper model evaluation and validation.
2) Ability to apply different supervided learning approaches for regression (such as linear and logistic regression).
3) Ability to apply different supervided learning approaches for classification (such as decsion trees. Naive bayes, and SVMs).
4) Ability to apply different unsupervided learning approaches for clustring (such as K-means, and KNN, and DBSCAN)
5) Ability to apply different ensemble learning approaches (such as Adaboost)
6) Ability to apply different approaches for handling imbalanced datasets (such as SMOTE and Borderline-SMOTE)
7) Ability to apply basic deep learning approaches such as FFNN, and CNN.
Course Content
For course details, see https://catalog2.metu.edu.tr.Course Learning Outcomes
1) SO (b) – PI-b1.
Infer facts and relationships from collected data.
2) SO (c) – PI-c2.
Evaluate and adapt standard algorithms algorithms (e.g. sorting, searching, string processing, language recognition, combination generation, and graph processing) for realistic tasks.
3) SO (e) – PI-c2.
Construct mathematical or logical models of computational problems.
4) SO (k) – PI-k5.
Use some special purpose languages and tools (such as those for mathematical programming, data manipulation and query, statistical analysis, hardware description, and simulation).
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Employ knowledge of mathematics, science and engineering to formulate solution to real life computing problems | ✔ | |||
| 2 | Design and conduct experiments, as well as analyze, evaluate and interpret data | ✔ | |||
| 3 | Design systems, components, and/or processes by specifying the requirements and determining the realistic constraints such as ethical and environmental | ✔ | |||
| 4 | Judge professional and ethical principles and integrate them in the working environment | ✔ | |||
| 5 | Have the ability to communicate effectively | ✔ | |||
| 6 | Recognize the need for, and an ability to engage in life-long learning | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution