Machine Learning II

2023/2024

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Educational Programmes

Course type:

Elective course

When:

2 year, 1 module

Instructor

Martinez-Saito, Mario

Course Syllabus

Abstract

Prerequisites: Basic knowledge of Machine Learning, Statistics and Python. This course aims to provide state-of-the-art techniques of mathematical statistics as well as new and modern methods of machine learning. What is more important, this course will focus on practical activities and it will allow students to learn on their mistakes. Moreover, skills and knowledge obtained during this course could be applied to almost any field of science and industry. Students will know statements of all major machine learning problems and mathematical details of the most important data analysis methods and algorithms. Also, they will obtain skills, such selection of an appropriate method for solving particular data analysis problems, performance of basic data processing and visual analysis, features generation for subsequent machine learning, application machine learning libraries, algorithm's selection hyperparameters, critically evaluate the obtained results and redesign data-processing pipelines, ability to solve real-world data science problems using modern machine learning techniques.

Learning Objectives

• Learn the basic concepts and methods of machine learning, and working knowledge of how to select and apply machine learning techniques to solve real world problems.

Expected Learning Outcomes

• Getting acquainted with the methods of model-free reinforcement learning, such as value learning.
• Recognizing and applying the most popular types of decision trees.
• Understanding how backpropagation works in neural networks with multiple layers.
• Deliver a presentation on a topic related to machine learning
• Understanding the basics of model-based reinforcement learning and its application to complex problems
• Applying machine learning methods to practical tasks such as data and text processing
• Learning how to configure neural nextworks for multiclass recognition and how to interpret patterns on high-dimensional spaces through dimensionality reduction techniques

Course Contents

• Model-free reinforcement learning
• Decision trees
• Neural networks
• Machine learning and deep learning history, frameworks, and news
• Model-based reinforcement learning
• Applied machine learning
• Neural networks and dimensionality reduction

Assessment Elements

• History, latest advances, development frameworks, coding project
• Exam
• Participation
  Active participation during the seminars

Interim Assessment

• 2023/2024 1st module
  0.4 * Exam + 0.3 * History, latest advances, development frameworks, coding project + 0.3 * Participation

Bibliography

Recommended Core Bibliography

• Pattern recognition and machine learning, Bishop, C. M., 2006

Recommended Additional Bibliography

• Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008