- 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)

- Reference #01 (Amari)

- lecture #09 (7. Radial_Basis_Function_Networks)

- Reference #02 (Lee and Kil)

- Reference #03 (Kil)

- 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 #15 (VCD_ANN_1)

- lecture #16 (VCD_ANN_2)

- lecture #17 (VCD_ANN_3)

- lecture #18 (VCD_MLP)

- lecture #19 (VCD_RBFN)

- 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)