460-2064/02 | Winter 2025/26
This course provides an introduction to the fundamental concepts of machine learning. Students will learn about supervised and unsupervised learning, model evaluation, and practical applications of machine learning algorithms.
Course content and its evaluation.
List of recommended sources.
This introductory lecture provides an overview of the course structure, content, and evaluation methods. It also includes a list of recommended textbooks and online resources to support student learning throughout the course.
Basic concepts of machine learning.
Overview of key principles.
This introductory lecture covers the motivation behind machine learning and the real-world examples that illustrate its importance. A dicussion on the various types of machine learning approaches will also be included.
Data and its significance
Difference data sources
This lecture delves into the importance of data in machine learning. Students will learn about different types of data, and their properties, and how to preprocess data for effective model training.
k-nearest neighbor algorithm
Applications and limitations
This lecture delves into the k-nearest neighbor algorithm. Students will learn about its applications in classification and regression tasks, as well as its limitations in terms of computational efficiency and sensitivity to noise.
Regression principles
Linear regression alghorithms
This lecture focus on the regression algoirthms, how it works, where to apply and how to get and interprete results. The Linear regression is duscissed in detail as well as evaluastion metrics used with regression data. Gradient descent for solving linear regression is also discussed.
Similarity and Distance understanding
Clustering algorithms
This lecture introduces into clustering algorithms. Describes the concept of similarity and distance between data points. K-Means will be discussed. Also importance of clustering interpretation will be discussed.
Similarity and Distance understanding
Clustering algorithms
This lecture introduces into clustering algorithms. Describes the concept of similarity and distance between data points. Hierarchical Clustering will be discussed. Also importance of clustering interpretation will be discussed.
Classification principles
Decision trees, Random forrests.
This lecture introduces classification principles, including nearest neighbor classification, Naive bayes algorithm, tree-based classification algorithms. Basic decision-tree algorithm is described in detail including split criteria. Ensable variants are introduced in lower detail with their benefits. Evaluation of the classification is also introduced.
Evaluation metrics for classification and regression
Precision, Recall, F1 Score.
This lecture introduces evaluation metrics for classification and regression tasks. Key metrics such as Precision, Recall, and F1 Score are defined and their importance in model evaluation is discussed. The lecture also covers the trade-offs between different metrics and how to choose the right metric for a given problem.
Overview of artificial neural networks
Architecture, training, and applications.
This lecture provides an overview of artificial neural networks (ANNs), including their architecture, training processes, and various applications. Students will learn about the fundamental components of ANNs, such as neurons, layers, and activation functions, as well as the backpropagation algorithm used for training. The lecture will also cover real-world applications of ANNs in fields like computer vision, natural language processing, and more.