PODCAST · education
Advanced Machine Learning
by ComputerScience
Machine learning (ML) is a field of computer science that allows systems to learn from experience and improve their performance. ML is used to solve problems that are difficult or impossible to program explicitly, such as speech recognition and navigating on Mars. ML is similar to statistics, but its focus is on building autonomous agents rather than helping humans draw conclusions. ML can be supervised (expected output is given) or unsupervised (no expected output given).
Episodes
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11. LLM
This lecture slideshow explores the world of Large Language Models (LLMs), detailing their architecture, training, and application. It begins by explaining foundational concepts like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) before moving on to Transformers, the...
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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 provides an overview of time series analysis, a powerful technique for understanding and forecasting data that evolves over time.…
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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 explores various examples of Seq2Seq problems, including machine translation, image captioning, and speech recognition. It then…
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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 approach, including error rate-based, statistical process control, and distance-based methods. The sources also delve into specific...
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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 with discriminative models, and then introduces the concept of adversarial training, explaining how GANs work. The notes then dive...
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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 slides introduce the concept of deep learning as a more advanced form of machine learning, highlighting its ability to learn...
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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 model for a different but related task. The slides explain different approaches to transfer learning, including fine-tuning...
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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 datasets. The curse of dimensionality refers to the challenges that arise when analyzing data with many features, such as...
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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 of simple perceptrons by incorporating hidden layers, allowing them to represent complex relationships in data. It discusses…
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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 explore their potential applications. The first text focuses on the basic structure of a neural network, particularly the...
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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 applications in different scenarios. The text then explores different types of machine learning, including supervised, unsupervised,...
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ABOUT THIS SHOW
Machine learning (ML) is a field of computer science that allows systems to learn from experience and improve their performance. ML is used to solve problems that are difficult or impossible to program explicitly, such as speech recognition and navigating on Mars. ML is similar to statistics, but its focus is on building autonomous agents rather than helping humans draw conclusions. ML can be supervised (expected output is given) or unsupervised (no expected output given).
HOSTED BY
ComputerScience
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Frequently Asked Questions
How many episodes does Advanced Machine Learning have?
Advanced Machine Learning currently has 11 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
What is Advanced Machine Learning about?
Machine learning (ML) is a field of computer science that allows systems to learn from experience and improve their performance. ML is used to solve problems that are difficult or impossible to program explicitly, such as speech recognition and navigating on Mars. ML is similar to statistics, but...
How often does Advanced Machine Learning release new episodes?
Advanced Machine Learning has 11 episodes. Check the episode list to see recent publication dates and frequency.
Where can I listen to Advanced Machine Learning?
You can listen to Advanced Machine Learning on PodParley by clicking any episode. We provide an embedded audio player for direct listening, and you can also subscribe via your preferred podcast app using the RSS feed.
Who hosts Advanced Machine Learning?
Advanced Machine Learning is created and hosted by ComputerScience.
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