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Main
UQU Programs
Data Science

Machine Learning for Data Science

DS2401
3 hours English

Machine Learning for Data Science

Machine Learning for Data Science
MTH2902 Applied Statistics for Data Science
3 hours One requisite
DS1103 Advanced Programming for Data Science
3 hours One requisite
1 Introduction to Machine Learning: / What is a Machine Learning? / Why Use Machine Learning Systems? / Types of Machine Learning Systems / Main Challenges of Machine Learning / Testing and Validating
2 Introduction to Scikit-Learn
3 Training a Binary Classifier / Classification Performance Measures / Confusion Matrix / Precision and Recall / Precision/Recall Tradeoff / The ROC Curve / Multiclass Classification / Multilabels Classification
4 Linear Regression and Logistic Regression
5 Classification using Support Vector Machines (SVMs): linear SVM , nonlinear SVM
6 Classification using Nearest Neighbors / Classification using Bayesian Classifier
7 Classification using Decision Trees
8 Ensemble Learning including Voting Classifiers, Bagging , Boosting, and Random Forests
9 Model Overfitting and underfitting / Model Selection and Training: / Training and Evaluating on the Training Set / Better Evaluation using Cross Validation including k-fold and leave-one-out approaches. / Methods for Comparing Classifiers
10 Association Analysis: Basic Concepts and Algorithms
11 Unsupervised Learning: / k-means Clustering Algorithm / Evaluating Clustering Algorithm / Different applications for clustering including Image segmentation, Using clustering for preprocessing and for semi-supervised learning
12 Building Machine Learning Pipelines / Fine-Tune Machine Learning model / Grid search / Randomized Search / Analyze the Best Models and their Errors / Evaluate on Test Set / Lunch and monitor and maintain Machine Learning System
13 Final Project Presentation
1.1 Mapped to: K1

Identify appropriate analysis tools and techniques.

Teaching Strategy Lectures Lab Exercises
Assessment Methods Midterm and Final exams Quiz Lab assessment
1.2 Mapped to: K2

Discuss the role of machine learning methods for solving real-world problems.

Teaching Strategy Lectures Lab Excerises
Assessment Methods Midterm and Final exams Quiz Lab assesment
1.3 Mapped to: K3

Explain the difference between different methods and algorithms used for data science

Teaching Strategy Lectures Lab Exercises
Assessment Methods Midterm and Final exams Quiz Lab assessment Project
2.1 Mapped to: S1

Apply appropriate machine learning techniques to support problem solving and decision making.

Teaching Strategy Lectures Lab Exercises
Assessment Methods Midterm and Final exams Quiz Lab exam Project
2.2 Mapped to: S3

Apply machine learning techniques to conduct research for real-world data science problems.

Teaching Strategy Lectures Lab Exercises
Assessment Methods Midterm and Final exams Lab assessment Project
2.3 Mapped to: S4

Show data interpretation abilities.

Teaching Strategy Lectures Lab Exercises
Assessment Methods Project
3.1 Mapped to: V2

Demonstrate ethical considerations of data science.

Teaching Strategy Lectures Lab Exercises
Assessment Methods Project
Assessment
Weight
Quizzes
15%
Midterm Exam
20%
Lab Assessment
10%
ML Project
15%
Final Exam
40%
Total
100%