miklosbona / machine-learning-course Goto Github PK

This repo contains materials for the introductory/intermediate Machine Learning (ML) courses (3 credits total) taught in the MSc in Business Analytics program at the Central European University (CEU), 2020. The material for 2018 and 2019 can be found on branches '2018' and '2019'. The material for the previous 2-credit course taught in 2016 and 2017 can be found here.

Zoltán Papp
Jenő Pál (TA)

Szilárd Pafka

The breakdown below (into ML #1 an #2) is somewhat adhoc and mainly to comply with administrative requirements. Both courses will intertwine general ML concepts, algorithms and software implementations/tools and will aim to strike a balance of theory and practice with the goal of equiping students with both the foundations to understand the ML methodology and also with the skills needed for using ML in practical business applications.

After an overview of the entire data science landscape this course will focus on machine learning. The course will introduce the main fundamental concepts in machine learning (supervised learning, training, scoring, accuracy measures, test set, overfitting, cross validation, model capacity, hyperparameter tuning, grid and random search, regularization, ensembles, model selection etc.) The concepts will be illustrated with R code therefore it requires prior familiarity with R.

This course will build on the previous one (which introduced the basic concepts in machine learning) and will discuss state-of-the-art algorithms for supervised learning (linear models, lasso, decision trees, random forests, gradient boosting machines, neural networks, support vector machine, deep learning etc.). A large part of the course will be dedicated to using (hands-on) the software tools for machine learning used by data scientists in practice (various high-performance R packages, xgboost, libraries for deep learning etc.).

The two courses are graded with the same structure but completely separately from each other.

• 45% Weekly Assignments (homework exercises). These will be submitted using Moodle.
• 45% Final Exam
• 10% Quizzes at the beginning of each lecture, except the first lectures of each course. Missing a lecture or being late will result in 0% for the actual quiz score.

Assignment acceptance policy and achievable grades:

• 100% until due date
• 50% within 24 hours past due date
• 0% after that.

Assignment dates and deadlines for ML #1:

• Feb 12, due on Feb 24 3:00 PM

Assignment dates and deadlines for ML #2:

• Feb 26, due on Mar 9 3:00 PM
• (tentative) Mar 18, due on Mar 27 3:00 PM

Final exam for ML1: Feb 19 1:30 PM Final exam for ML2: Mar 30 3.30 PM

Class announcements and student Q&A will be done via Moodle.

• Course site for ML1.

ML 1.1: Lab: penalized linear models: ridge, LASSO, elastic net. Lecture | Lab.

ML 1.2: Lab: Unsupervised learning. Clustering (k-means, hierarchical). PCA. Lecture | Lab.

ML 2.1: Understanding and tuning parameters for trees, random forests and gradient boosting machines. Impact of correlated features. Support vector machines. Tools: R packages, xgboost, lightgbm Lecture | Lab.

ML 2.2: Neural networks and deep learning. Reinforcement Learning. Evolutionary Computing. Tools: R packages, Keras. Lecture | Lab.

ML 2.3: Ensembles, Stacking. Lecture | Lab.

ML 2.4: Recap and summary. Lecture | Lab.