Learning Machines 101
Celková dĺžka:
19 h 30 min
LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
Learning Machines 101
35:29
LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
Learning Machines 101
30:51
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
Learning Machines 101
33:13
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
Learning Machines 101
34:22
LM101-082: Ch4: How to Analyze and Design Linear Machines
Learning Machines 101
29:05
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
Learning Machines 101
37:20
LM101-080: Ch2: How to Represent Knowledge using Set Theory
Learning Machines 101
31:43
LM101-079: Ch1: How to View Learning as Risk Minimization
Learning Machines 101
26:07
LM101-078: Ch0: How to Become a Machine Learning Expert
Learning Machines 101
39:18
LM101-077: How to Choose the Best Model using BIC
Learning Machines 101
24:15
LM101-076: How to Choose the Best Model using AIC and GAIC
Learning Machines 101
28:17
LM101-075: Can computers think? A Mathematician's Response (remix)
Learning Machines 101
36:26
LM101-074: How to Represent Knowledge using Logical Rules (remix)
Learning Machines 101
19:22
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
Learning Machines 101
24:58
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
Learning Machines 101
22:07
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
Learning Machines 101
31:40
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
Learning Machines 101
32:04
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
Learning Machines 101
23:20
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
Learning Machines 101
21:49
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
Learning Machines 101
25:40
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
Learning Machines 101
34:00
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
Learning Machines 101
30:00
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
Learning Machines 101
28:04
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
Learning Machines 101
22:04
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
Learning Machines 101
31:05
LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
Learning Machines 101
29:15
LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
Learning Machines 101
29:32
LM101-059: How to Properly Introduce a Neural Network
Learning Machines 101
29:56
LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
Learning Machines 101
19:38
LM101-057: How to Catch Spammers using Spectral Clustering
Learning Machines 101
19:54
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
Learning Machines 101
27:59
LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
Learning Machines 101
35:06
LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
Learning Machines 101
29:35
LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
Learning Machines 101
26:50
LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
Learning Machines 101
28:57
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun]
Learning Machines 101
29:04
LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
Learning Machines 101
30:32
LM101-049: How to Experiment with Lunar Lander Software
Learning Machines 101
34:40
LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
Learning Machines 101
31:27
LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)
Learning Machines 101
35:29