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    университет

Machine Learning

Machine Learning

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Course Information

CourseMachine Learning CodeMLNB351
Directions60610100 – Computer Sciences and Programming Technologies Form of StudyFull-time
Semester6 Total Hours, Credit150, 5
Lectures30 Practical Lessons30
Subject Teacher Sapura Sattarova Taught LanguageEnglish

Lectures – Semester VI

M1Introduction to Machine Learning Concepts: Overview of what machine learning is, its types, and where it is used.Download
M2Data Preprocessing: Basics of cleaning data, dealing with missing values, and scaling features.Download
M3Feature Engineering and Selection: How to create new features and select the most important ones for models.Download
M4Linear Regression: Introduction to predicting outcomes with linear regression.Download
M5Logistic Regression: Basics of classification using logistic regression Download
M6Decision Trees: Simple classification and regression models using decision trees.Download
M7k-Nearest Neighbors (k-NN): An easy classification algorithm based on finding the closest neighbors.Download
M8Support Vector Machines (SVM): Introduction to classifying data by finding the optimal separation boundary.Download
M9Model Evaluation Techniques: Understanding how to evaluate models using metrics like accuracy, precision, recall, etc.Download
M10Cross-Validation: How to split data into training and testing sets, and improve model performance through cross-validation.Download
M11Clustering Algorithms: Introduction to grouping data using algorithms like K-Means and DBSCAN.Download
M12Dimensionality Reduction: How to reduce the number of features using techniques like Principal Component Analysis (PCA).Download
M13Introduction to Artificial Neural Networks: Basic understanding of neural networks and how they work.Download
M14Overfitting and Underfitting: How to identify when a model is overfitting or underfitting, and techniques to address them.Download
M15Bias and Fairness in Machine Learning: Ethical concerns related to biased datasets and models in machine learning.Download

Practical Lessons – Semester VI

CodeTopicMaterial
P1Hands-On with Machine Learning Applications: Explore simple real-world applications of machine learning, such as in retail and healthcareDownload
P2Data Cleaning and Preprocessing in Python: Use Python (Pandas, Scikit-learn) to clean data and handle missing values.Download
P3Feature Creation and Selection with Python: Practically create new features and select important ones using Python libraries.Download
P4Implementing Linear Regression: Build and test a linear regression model using Scikit-learn.Download
P5Logistic Regression in Python: Implement a basic classification task with logistic regression on a simple dataset.Download
P6Building Decision Trees: Use Scikit-learn to implement decision trees for both classification and regression.Download
P7Using k-Nearest Neighbors (k-NN): Build a k-NN classifier and explore how changing parameters affects resultsDownload
P8Simple Support Vector Machines (SVM) Implementation: Build an SVM model for classification and understand the use of different kernels.Download
P9Evaluating Models in Python: Calculate accuracy, precision, recall, and F1-score for different models.Download
P10Cross-Validation on Real-World Data: Apply k-fold cross-validation on a dataset to improve model evaluation.Download
P11Clustering with K-Means and DBSCAN: Perform clustering on a dataset and visualize the clusters using Python.Download
P12Reducing 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
P14Overfitting and Underfitting with Python: Experiment with overfitting and underfitting by adjusting model complexity and regularization.Download
P15Exploring Bias in a Dataset: Analyze a dataset to detect bias and apply techniques to minimize it.Download

Refrences

TypeReference
MainNilsson, N. J. Introduction to Machine Learning. . Stanford University, 1996. https://ai.stanford.edu/~nilsson/MLBOOK.pdf.
MainBishop, 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.
MainSutton, 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.
AdditionalZaynidinov H.N., Xo‘jaqulov T.A., Atadjanova M.P. WSun’iy intellekt. — Toshkent: Aloqachi, 2018. — 268 b.. ISBN 978-9943-5486-9-5.
AdditionalSadullayeva 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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