MNG 623_machine_learning
This course covers the fundamentals of modern statistical machine learning. Lectures will cover fundamental aspects of machine learning, including dimensionality reduction, overfitting, ensemble learning, and evaluation techniques, as well as the theoretical foundation and algorithmic details of representative topics within clustering, regression, and classification (for example, K-Means clustering, Support Vector Machines, Decision Trees, Linear and Logistic Regression, Neural Networks, among others). Students will be expected to perform theoretical derivations and computations, and to be able to implement algorithms from scratch. The course will conclude with a final project and presentation on a machine learning problem of their choosing.
|
|
Resources
|
Lectures |
|
Week 1 |
01_Introduction to Artificial Intelligent |
|
Week 2 |
02_components of learning |
|
Week 3 |
03_Learning_feasability |
|
Week 4 |
04_Linear Regression |
|
Week 5 |
05_Linear Regression_II |
|
Week 6 |
06_logistic_Regression |
|
Week 7 |
07_Probability Theory |
|
08_Data_preprocessing |
||
Week 8 |
09_SVM |
|
Week 9 |
10_decisionTree |
|
Week 10 |
11_Ensemble_Learning |
|
Week 11 |
12_Introduction to Clustering |
|
Week 12 |
13_PCA |
|
Week 13 |
14.KNN |
Exams and Assignments
|
Exams |
|
Week 7 |
Midterm exam |