Special Topics 1 - Machine Learning - 10636464
Course Title
Special Topics 1 - Machine Learning
Course Number
Instructor Name
Anas Toma
Contact Information
[email protected]
Semester(s) and academic year(s)
First Semester 2022
Second Semester 2021
Compulsory / Elective
Course Description
  • Introduction
  • Fundamentals
  • Data Representation and Understanding
    • Data types, description, similarity and visualization
  • Data Preprocessing
    • Data cleaning, integration, reduction and transformation
  • Frequent Patterns, Association and Correlations
  • Supervised Learning – Classification
    • Linear Regression
    • Decision Trees
    • Random Forests
    • Rule-Based Classification
    • Support Vector Machines
    • K-Nearest-Neighbor (K-NN)
    • Genetic Algorithms
    • Model Evaluation and Selection
    • Improving Classification Accuracy
  • Unsupervised Learning – Clustering
    • Partitioning, Hierarchical, Density-Based and Grid-Based Methods
  • Deep Learning
  • Outlier Analysis and Detection
  • Prediction
Course Objectives

This course presents the main techniques used in machine learning and data analysis. It will cover all the necessary steps from handling raw data until having an intelligent system that can learn from experience. The students will be able to design, implement and evaluate computer systems that can learn to recognize patterns from data and make intelligent decisions. Furthermore, they will acquire the necessary skills for data analysis, preprocessing and visualization. The techniques will be explored using real-world data sets and different programming libraries such as Scikit-Learn, TensorFlow and Keras.

Intended learning Outcomes and Competences

At the end of this course, the students will be able to:

  • Analyze, prepare and visualize the input data.
  • Select the appropriate machine learning techniques to solve real-world problems.
  • Implement the machine learning techniques using different programming libraries.
  • Evaluate the techniques to improve the performance and the quality of the results.
Textbook and References


  • Aurélien Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 2nd Edition. O'Reilly Media, 2019. ISBN: 1492032646, 978-1492032649.
  • Jiawei Han, Jian Pei, Micheline Kamber. Data Mining: Concepts and Techniques. 3rd Edition. Elsevier, 2011. ISBN: 0123814804, 9780123814807.


  • Scikit-Learn, TensorFlow and Keras.
Assessment Criteria
Activity Percent (%)
Midterm exam 30%
Activities 10%
Project/ HWs 20%
Final Exam 40%