Learning Machines 101
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Learning Machines 101
Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In th...
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85 эпизодLM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of outcomes in a space-time con...
LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
This 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed to minimi...
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
In this episode of Learning Machines 101, we review Chapter 6 of my book “Statistical Machine Learning” which introduces methods for analyzing the beh...
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
This particular podcast covers the material from Chapter 5 of my new book “Statistical Machine Learning: A unified framework” which is now available!...
LM101-082: Ch4: How to Analyze and Design Linear Machines
The main focus of this particular episode covers the material in Chapter 4 of my new forthcoming book titled “Statistical Machine Learning: A unified...
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
This particular podcast covers the material in Chapter 3 of my new book “Statistical Machine Learning: A unified framework” with expected publication...
LM101-080: Ch2: How to Represent Knowledge using Set Theory
This particular podcast covers the material in Chapter 2 of my new book “Statistical Machine Learning: A unified framework” with expected publication...
LM101-079: Ch1: How to View Learning as Risk Minimization
This particular podcast covers the material in Chapter 1 of my new (unpublished) book “Statistical Machine Learning: A unified framework”. In this epi...
LM101-078: Ch0: How to Become a Machine Learning Expert
This particular podcast (Episode 78 of Learning Machines 101) is the initial episode in a new special series of episodes designed to provide commentar...
LM101-077: How to Choose the Best Model using BIC
In this 77th episode of www.learningmachines101.com , we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and em...
LM101-076: How to Choose the Best Model using AIC and GAIC
In this episode, we explain the proper semantic interpretation of the Akaike Information Criterion (AIC) and the Generalized Akaike Information Criter...
LM101-075: Can computers think? A Mathematician's Response (remix)
In this episode, we explore the question of what can computers do as well as what computers can’t do using the Turing Machine argument. Specifically,...
LM101-074: How to Represent Knowledge using Logical Rules (remix)
In this episode we will learn how to use “rules” to represent knowledge. We discuss how this works in practice and we explain how these ideas are impl...
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
This is a remix of the original second episode Learning Machines 101 which describes in a little more detail how the computer program that Arthur Samu...
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
This podcast is basically a remix of the first and second episodes of Learning Machines 101 and is intended to serve as the new introduction to the Le...
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense into learning...
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique call...
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
This 69th episode of Learning Machines 101 provides a short overview of the 2017 Neural Information Processing Systems conference with a focus on the...
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
This 68th episode of Learning Machines 101 discusses a broad class of unsupervised, supervised, and reinforcement machine learning algorithms which it...
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
In this episode we discuss how to learn to solve constraint satisfaction inference problems. The goal of the inference process is to infer the most pr...
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
In this episode of Learning Machines 101 (www.learningmachines101.com) we discuss how to solve constraint satisfaction inference problems where knowle...
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
In this episode rerun we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of deep lear...
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
In this rerun of episode 24 we explore the concept of evolutionary learning machines. That is, learning machines that reproduce themselves in the hope...
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
This 63rd episode of Learning Machines 101 discusses how to build reinforcement learning machines which become smarter with experience but do not use...
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
This 62nd episode of Learning Machines 101 (www.learningmachines101.com) discusses how to design reinforcement learning machines using your knowledge...
LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
This is the third of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Inform...
LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
This 60th episode of Learning Machines 101 discusses how one can use novelty detection or anomaly detection machine learning algorithms to monitor the...
LM101-059: How to Properly Introduce a Neural Network
I discuss the concept of a “neural network” by providing some examples of recent successes in neural network machine learning algorithms and providing...
LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
In this 58th episode of Learning Machines 101, I’ll be discussing an important new scientific breakthrough published just last week for the first time...
LM101-057: How to Catch Spammers using Spectral Clustering
In this 57th episode, we explain how to use unsupervised machine learning algorithms to catch internet criminals who try to steal your money electroni...
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
In this NEW episode we discuss Latent Semantic Indexing type machine learning algorithms which have a PROBABILISTIC interpretation. We explain why su...
LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning machines that...
LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
Welcome to the 54th Episode of Learning Machines 101 titled "How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Lat...
LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
In this 53rd episode of Learning Machines 101, we introduce the concept of a Swarm Intelligence with respect to Particle Swarm Optimization Algorithms...
LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
Today, we discuss a simple yet powerful idea which began popular in the machine learning literature in the 1990s which is called “The Kernel Trick”. T...
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun]
This particular podcast is a RERUN of Episode 20 and describes step by step how to download free software which can be used to make predictions using...
LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
In this episode we will explain how to download and use free
machine learning software from the website: www.learningmachines101.com.
This...
LM101-049: How to Experiment with Lunar Lander Software
In this episode we continue the discussion of learning when the actions of the learning machine can alter the characteristics of the learning machine’...
LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
In this episode we consider the problem of learning when the actions of the learning machine can alter the characteristics of the learning machine’s s...
LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)
We explain how to estimate the parameters of such machines to classify a pattern vector as a member of one of two categories as well as identify speci...