LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes

LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges

LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems

LM101-083: Ch5: How to Use Calculus to Design Learning Machines

LM101-082: Ch4: How to Analyze and Design Linear Machines

LM101-081: Ch3: How to Define Machine Learning (or at Least Try)

LM101-080: Ch2: How to Represent Knowledge using Set Theory

LM101-079: Ch1: How to View Learning as Risk Minimization

LM101-078: Ch0: How to Become a Machine Learning Expert

LM101-077: How to Choose the Best Model using BIC

LM101-076: How to Choose the Best Model using AIC and GAIC

LM101-075: Can computers think? A Mathematician's Response (remix)

LM101-074: How to Represent Knowledge using Logical Rules (remix)

LM101-073: How to Build a Machine that Learns to Play Checkers (remix)

LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)

LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets

LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding

LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?

LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms

LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)