2024/2025

Machine Learning

Type: Mago-Lego

Delivered by: Big Data and Information Retrieval School

When: 1, 2 module

Open to: students of one campus

Instructors: Maksim Karpov

Language: English

ECTS credits: 6

Course Syllabus

Abstract

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods. Students will apply Python programming language and popular packages, such as pandas, scikit-learn, to investigate and visualize datasets and develop machine learning models that solve theoretical and data-driven problems. Pre-requisites: at least one semester of calculus on a real line, vector calculus, linear algebra, probability and statistics, computer programming in high level language such as Python.

Learning Objectives

• The course aims to help students develop an understanding of the process to learn from data, familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.

Expected Learning Outcomes

• Form an understanding of the core tools of the course
• Gained experience in learning, presenting, reviewing and discussing a paper, a deep understanding of the NeRF / Word2Vec overview
• Gained experience in learning, presenting, reviewing and discussing a paper, a deep understanding of the Attention Is All you need / Model-agnostic meta-learning for fast adaptation of deep networks
• Gained experience in learning, presenting, reviewing and discussing a paper, a deep understanding of the CLIP / GAN
• Gained experience in learning, presenting, reviewing and discussing a paper, a deep understanding of the Typical Decoding / NeRF
• Gained experience in learning, presenting, reviewing and discussing papers, a deep understanding of the Robustness May be at Odds with Accuracy
• Construct machine learning models on the proposed data sets in Python.
• Evaluate performance of the models.
• Build features suitable for the selected machine learning models.
• Tune models to improve prediction and classification performance of the models.

Course Contents

• Introduction to Machine Learning
• Linear Regression and K-Nearest Neighbors (KNN)
• Classification with Logistic Regression, KNN
• Resampling methods, CrossValidation, Bootstrap
• Linear model selection, Regularization
• Decision Trees, Bagging, Random Forest, Boosting.
• Support Vector Machines/Classifiers.
• Clustering methods. PCA, k-Means, Hierarchical Clustering, DBSCAN.

Assessment Elements

• Home Assignments
  Stack Maxima problems with writing the formulas and auto grading, Kaggle competitions. The grade for the current category is calculated as cumulative from the beginning of the course.
• Exam
  This is the individualized exam. In general, expect 60 questions, some of which you may will have seen in quizzes. The assessment of the exam is based on the marking scheme that comes with the exam assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 10, which is the maximum grade for the exam on the 10-point scale. The student is awarded the assigned number of points for the correct answer to each part of the question and partial credit may also be awarded. The grade for the current category is calculated as cumulative from the beginning of the course.
• Midterm Test
  These are individualized tests. The assessment of the test is based on the marking scheme that comes with the test assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 10, which is the maximum grade for the test on the 10- point scale. The student is awarded the assigned number of points for the correct answer to each part of the question and partial credit may also be awarded. The grade for the current category is calculated as cumulative from the beginning of the course.

Interim Assessment

• 2024/2025 2nd module
  0.3 * Exam + 0.2 * Home Assignments + 0.3 * Home Assignments + 0.2 * Midterm Test

Bibliography

Recommended Core Bibliography

• Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning : With Applications in R. Springer.
• James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.

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

Authors

• Ахмедова Гюнай Интигам кызы