DAMSL-101 Introduction to Machine Learning

Type

Core

Course Code

DAMSL-101

Teaching Semester

A semester

ECTS Credits

Student Performance Evaluation

Homework and/or Lab assignments, Final exam and/or project

Prerequisite Courses

Probabilities, Data Structures

Syllabus

Introduction to ML, supervised, unsupervised, reinforcement learning, hypothesis (models) spaces, examples of ML applications
Probability theory and concepts for ML, axioms of probability, conditional probability, Bayes theorem, maximum likelihood estimation, maximum a posteriori estimation
Logistic Regression and fitting with gradient descent
Hypothesis testing, and permutation-based hypothesis testing
Naïve Bayes
Decision Trees and Random Forests
Metrics of performance, Receiver Operating Characteristic Curves (ROC), and Area Under the ROC curve
Estimation of performance, hyper-parameter tuning, and introduction to Automated Machine Learning (AutoML)
Basics of optimization and constrained optimization and Support Vector Machine classification
Introduction to causal discovery and causal-based feature Selection

Learning Outcomes

The purpose of the course is to provide a broad introduction to the field of Machine Learning including the basic theory, principles, and algorithms, as well as practical applications on real problems. Upon successful completion of this course students should be able to:

Learn basic ML tasks and types of analysis, such as supervised learning, unsupervised learning, reinforcement learning, classification and regression, and feature selection.
Understand the inner workings of standard ML classification and feature selection algorithms
Learn how to solve the problem of selecting algorithms, tuning their hyper-parameters, and estimating the performance of the final predictive model.
Be able to perform and apply ML pipelines to real-world problems, dealing with problems such as representing the problems as an ML task, representing appropriately the data, applying and tuning an ML pipeline, and interpreting results.
Know key statistical estimation and hypothesis testing concepts, with a focus on the ones that are routinely employed within ML algorithms
Have a solid, foundational basis to perform ML research and proceed with other courses that employ ML algorithms and concepts