Ranked #1
LM101-004: Can computers think? A mathematician.s response
LM101-004: Can computers think? A mathematician.s response
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this e... Read more
12 May 2014
•
34mins
Ranked #2
LM101-078: Ch0: How to Become a Machine Learning Expert
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... Read more
24 Oct 2019
•
39mins
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Ranked #3
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
In this episode, we discuss the problem of predicting the future from not only recent events but also from the distant p... Read more
28 Sep 2015
•
25mins
Ranked #4
LM101-059: How to Properly Introduce a Neural Network
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 lea... Read more
21 Dec 2016
•
29mins
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Ranked #5
LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
We discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered c... Read more
26 Jan 2015
•
35mins
Ranked #6
LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory
LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In real l... Read more
23 Jun 2014
•
26mins
Ranked #7
LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)
LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this p... Read more
12 Aug 2014
•
34mins
Ranked #8
LM101-006: How to Interpret Turing Test Results
LM101-006: How to Interpret Turing Test Results
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this e... Read more
9 Jun 2014
•
31mins
Ranked #9
LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)
LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)
In this episode, we discuss the problem of how to build a machine that can do anything! Or more specifically, given a se... Read more
13 Oct 2014
•
32mins
Ranked #10
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
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 adva... Read more
31 Jan 2018
•
32mins
Ranked #11
LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions
LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions
In this episode we introduce some advanced nonlinear machine software which is more complex and powerful than the linear... Read more
12 Jan 2015
•
27mins
Ranked #12
LM101-075: Can computers think? A Mathematician's Response (remix)
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 Ma... Read more
12 Dec 2018
•
36mins
Ranked #13
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
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 machin... Read more
26 Sep 2017
•
21mins
Ranked #14
LM101-008: How to Represent Beliefs Using Probability Theory
LM101-008: How to Represent Beliefs Using Probability Theory
Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using ... Read more
3 Sep 2014
•
30mins
Ranked #15
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
Deep learning machine technology has rapidly developed over the past five years due in part to a variety of actors such ... Read more
8 Jun 2015
•
32mins
Ranked #16
LM101-024: How to Use Genetic Algorithms to Breed Learning Machines
LM101-024: How to Use Genetic Algorithms to Breed Learning Machines
In this episode we introduce the concept of learning machines that can self-evolve using simulated natural evolution int... Read more
10 Mar 2015
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29mins
Ranked #17
LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)
LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)
In this episode we discuss the problem of how to evaluate the ability of a learning machine to make generalizations and ... Read more
9 Sep 2014
•
32mins
Ranked #18
LM101-077: How to Choose the Best Model using BIC
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 Info... Read more
2 May 2019
•
24mins
Ranked #19
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
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 learnin... Read more
19 Jun 2017
•
30mins
Ranked #20
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
In this episode we introduce a very powerful approach for computing semantic similarity between documents. Here, the te... Read more
24 Nov 2015
•
28mins
