Advanced Machine Learning

11. LLM

This lecture slideshow explores the world of Large Language Models (LLMs), detailing their architecture, training, and application. It begins by explaining foundational c

10. Time Series

Forecasting, the process of predicting future events, is a fundamental element of many disciplines, including economics, meteorology, and social sciences. This text provi

09. Seq to Seq

This source is a lecture on sequence-to-sequence learning (Seq2Seq), a technique for training models to transform sequences from one domain to another. The lecture explor

08. Drift Detection

The source material explores the challenges and techniques for detecting concept drift in machine learning models. It examines several methods categorized by their approa

07. - Generative Adversarial Networks (GANs)

The source is a series of lecture notes on Generative Adversarial Networks (GANs). It begins with an introduction to generative models, comparing and contrasting them wit

06. Introduction to Basic Deep learning

The provided text, excerpts from "06. Introduction to Basic Deep Learning (Slides).pdf," is a series of lecture slides covering the fundamentals of deep learning. The sli

05. Transfer Learning

These lecture slides discuss transfer learning in machine learning, which is a technique that reuses a pre-trained model for one task to improve the performance of a new

04. Dimensionality Reduction

The source describes dimensionality reduction, a technique used to simplify and improve the performance of machine learning algorithms when dealing with high-dimensional

03. Neural Networks Continued

The source material focuses on the development and training of neural networks. The first source introduces multilayer perceptrons (MLPs), which overcome the limitations

02. Introduction to Neural Networks

The two source texts, "02. Introduction to Neural Networks (Slides).pdf" and "02.2 Optional Slides.pdf", provide an introduction to the concept of neural networks and exp

01. Machine Learning Basics

This source is a comprehensive introduction to machine learning, covering various aspects of the field. It starts by explaining the core concept of learning and its appli