Machine Learning

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Denard Veshi, Prof. Asoc. Dr.

Code
CMP 501
Name
Machine Learning
Semester
3
Lecture hours
3.00
Seminar hours
1.00
Laborator hours
0.00
Credits
3.50
ECTS
6.00
Description

The “Machine Learning” course introduces the fundamental principles and main techniques of learning from data. It covers both supervised and unsupervised algorithms and their practical applications in classification, regression, clustering, and dimensionality reduction.

Objectives

To understand the mathematical and statistical foundations of Machine Learning. To apply supervised and unsupervised algorithms to real-world problems. To evaluate model performance and understand overfitting/underfitting. To use modern tools and libraries such as Scikit-learn, TensorFlow, Keras.

Java
Tema
1
Introduction to Machine Learning and Applications
2
Statistical and Probabilistic Foundations
3
Linear and Logistic Regression
4
Classification with KNN and Naive Bayes
5
SVM and Separating Hyperplanes
6
Decision Trees and Random Forests
7
Model Performance Evaluation
8
Midterm Exam
9
Clustering Algorithms: K-Means, DBSCAN
10
Dimensionality Reduction: PCA and t-SNE
11
Neural Networks and Backpropagation
12
Overfitting, Underfitting, and Regularization
13
ML with Scikit-learn and Keras
14
Practical Applications and Case Studies
15
Project Presentations
16
Final Exam
1
Students will understand and apply commonly used Machine Learning algorithms.
2
They will be able to analyze data and build predictive models.
3
They will evaluate and improve model performance through advanced techniques.
4
They will use powerful libraries to implement practical solutions in Python.
Quantity Percentage Total percent
Midterms
0 0% 0%
Quizzes
0 0% 0%
Projects
1 25% 25%
Term projects
0 0% 0%
Laboratories
0 0% 0%
Class participation
1 10% 10%
Total term evaluation percent
35%
Final exam percent
65%
Total percent
100%
Quantity Duration (hours) Total (hours)
Course duration (including exam weeks)
16 4 64
Off class study hours
14 5 70
Duties
1 10 10
Midterms
0 0 0
Final exam
1 6 6
Other
0 0 0
Total workLoad
150
Total workload / 25 (hours)
6.00
ECTS
6.00