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Regularization for linear models
00:03:15
Linear discrimination: summary and connections to other models
00:06:35
Ensemble models with decision trees: bagging, random forests, extra trees, boosted trees
00:22:36
Pruning decision trees
00:22:07
Feature ranking with decision trees
00:05:05
Evaluate a split for decision trees
00:06:59
Impurity measures for decision trees regressors
00:04:46
Impurity measures for decision trees classifiers
00:28:11
2.6 Closure properties of recognisable languages
00:23:37
2.5.1 Removing epsilon transitions from nondeterministic finite automata
00:32:31
2.7.1 The minimization of finite automata: an intuitive perspective
00:21:22
3.3 From automata to regular expressions: language equations
00:31:49
3.4 Non-regular languages
00:22:03
4.2 Right linear grammars
00:25:31
3.2 From regular expressions to finite automata and back. Reg = Rec.
00:23:06
3.1 Regular expressions
00:17:29
2.7.2 The minimisation of finite automata: the formal construction
00:33:33
2.5.3 DFA=NFA. The subset construction: an example
00:11:40
2.5.2 From a nondeterministic finite automaton to a deterministic one: the subset construction
00:26:18
2.5.0 Equivalence of nondeterministic and deterministic finite automata: an intuitive example
00:24:21
2.4.2 Nondeterministic finite automata: formal definition
00:27:24
2.4.1 Nondeterministic finite automata: an intuition
00:25:34
2.3.2 Deterministic finite automata: formal definition
00:29:37
2.3.1 What is a finite automaton?
00:18:40
2.2 Language operations
00:21:56
2.1 Automata basic definitions: alphabet, words, languages
00:19:36
AI in your everyday life: how to discuss AI with high school students
00:29:20
Gradient descent for classification problems
00:18:24
Logistic regression: basic concepts
00:14:24
Gradient descent for regression problems
00:23:24