- lecture#01 (1. introduction)
- lecture #02 (2_1. Concept_Learning_A)
- lecture #03 (2_2. Concept_Learning_B)
- lecture #04 (3. Decision_Tree_Learning)
- lecture #05 (4. Perceptron_A)
- lecture #06 (4. Perceptron_B)
- lecture #07 (5. Supervised_Learning_Algorithms)
- lecture #08 (6. Multilayer_Perceptrons)
- lecture #09 (7. Radial_Basis_Function_Networks)
- lecture #10 (8. Hypothesis_Evaluation_A)
- lecture #11 (8. Hypothesis_Evaluation_B)
- lecture #12 (9. Computational_Learning_Theories_A)
- lecture #13 (9. Computational_Learning_Theories_B)
- lecture #14 (9. Chernoff_Hoeffding_Bounds)
- lecture #20 (Support_Vector_Machines)
- lecture #21 (Bayesian_Belief_Networks_A)
- lecture #22 (Bayesian_Belief_Networks_B)
- lecture #23 (Baggin&Boosting)
- lecture #24 (Evolutionary_Computation)