Urganch Davlat Universiteti - Machine Learning

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Universitet

Machine Learning

Machine Learning

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Course Information

Course	Machine Learning	Code	MLNB351
Directions	60610100 – Computer Sciences and Programming Technologies	Form of Study	Full-time
Semester	6	Total Hours, Credit	150, 5
Lectures	30	Practical Lessons	30
Subject Teacher	Sapura Sattarova	Taught Language	English

Lectures – Semester VI

M1	Introduction to Machine Learning Concepts: Overview of what machine learning is, its types, and where it is used.	Download
M2	Data Preprocessing: Basics of cleaning data, dealing with missing values, and scaling features.	Download
M3	Feature Engineering and Selection: How to create new features and select the most important ones for models.	Download
M4	Linear Regression: Introduction to predicting outcomes with linear regression.	Download
M5	Logistic Regression: Basics of classification using logistic regression	Download
M6	Decision Trees: Simple classification and regression models using decision trees.	Download
M7	k-Nearest Neighbors (k-NN): An easy classification algorithm based on finding the closest neighbors.	Download
M8	Support Vector Machines (SVM): Introduction to classifying data by finding the optimal separation boundary.	Download
M9	Model Evaluation Techniques: Understanding how to evaluate models using metrics like accuracy, precision, recall, etc.	Download
M10	Cross-Validation: How to split data into training and testing sets, and improve model performance through cross-validation.	Download
M11	Clustering Algorithms: Introduction to grouping data using algorithms like K-Means and DBSCAN.	Download
M12	Dimensionality Reduction: How to reduce the number of features using techniques like Principal Component Analysis (PCA).	Download
M13	Introduction to Artificial Neural Networks: Basic understanding of neural networks and how they work.	Download
M14	Overfitting and Underfitting: How to identify when a model is overfitting or underfitting, and techniques to address them.	Download
M15	Bias and Fairness in Machine Learning: Ethical concerns related to biased datasets and models in machine learning.	Download

Practical Lessons – Semester VI

Code	Topic	Material
P1	Hands-On with Machine Learning Applications: Explore simple real-world applications of machine learning, such as in retail and healthcare	Download
P2	Data Cleaning and Preprocessing in Python: Use Python (Pandas, Scikit-learn) to clean data and handle missing values.	Download
P3	Feature Creation and Selection with Python: Practically create new features and select important ones using Python libraries.	Download
P4	Implementing Linear Regression: Build and test a linear regression model using Scikit-learn.	Download
P5	Logistic Regression in Python: Implement a basic classification task with logistic regression on a simple dataset.	Download
P6	Building Decision Trees: Use Scikit-learn to implement decision trees for both classification and regression.	Download
P7	Using k-Nearest Neighbors (k-NN): Build a k-NN classifier and explore how changing parameters affects results	Download
P8	Simple Support Vector Machines (SVM) Implementation: Build an SVM model for classification and understand the use of different kernels.	Download
P9	Evaluating Models in Python: Calculate accuracy, precision, recall, and F1-score for different models.	Download
P10	Cross-Validation on Real-World Data: Apply k-fold cross-validation on a dataset to improve model evaluation.	Download
P11	Clustering with K-Means and DBSCAN: Perform clustering on a dataset and visualize the clusters using Python.	Download
P12	Reducing Dimensions with PCA: Implement PCA to reduce the features of a dataset and evaluate the impact on model performance.	Download
P13	Creating Simple Neural Networks with TensorFlow/Keras: Build a simple neural network to classify images or predict outcomes.	Download
P14	Overfitting and Underfitting with Python: Experiment with overfitting and underfitting by adjusting model complexity and regularization.	Download
P15	Exploring Bias in a Dataset: Analyze a dataset to detect bias and apply techniques to minimize it.	Download

Refrences

Type	Reference
Main	Nilsson, N. J. Introduction to Machine Learning. . Stanford University, 1996. https://ai.stanford.edu/~nilsson/MLBOOK.pdf.
Main	Bishop, C. M. Pattern Recognition and Machine Learning. . New York: Springer, 2006. https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf.
Main	Sutton, R. S., Barto, A. G. Reinforcement Learning: An Introduction. 2nd ed. — Cambridge, MA: MIT Press 2018.. URL: https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf.
Additional	Zaynidinov H.N., Xo‘jaqulov T.A., Atadjanova M.P. WSun’iy intellekt. — Toshkent: Aloqachi, 2018. — 268 b.. ISBN 978-9943-5486-9-5.
Additional	Sadullayeva Sh.A., Yusupov D.F., Yusupov F. Sun’iy intellekt va neyronto‘rli texnologiyalar. — Toshkent: Mahalla va oila . 2022. — 192 b. — ISBN 978-9943-7728-7-8.

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