Machine Learning notes (6.867, Fall 2013)

Lecture notes from 6.867 Machine Learning, taught by Prof. Tommi Jaakkola.

WARNING: These are unpolished notes and may contain mistakes.

• 2013.09.10.w2.tu-lecture-2-linear-classifier.pdf
• 2013.09.12.w2.th-lecture-3-svm.pdf
• 2013.09.17.w3.tu-lecture-4-kernels-non-linear-svm.pdf
• 2013.09.19.w3.th-lecture-5-more-kernels-lagrange.pdf
• 2013.09.24.w4.tu-lecture-6-linear-regression-model-selection.pdf
• 2013.09.26.w4.th-lecture-7-ml-map-bayesian-gaussian-proc.pdf
• 2013.10.01.w5.tu-lecture-8-gaussian-processes-regression-trees.pdf
• 2013.10.03.w5.th-lecture-9-classification-trees-ensemble-methods.pdf
• 2013.10.08.w6.tu-lecture-10-boosting.pdf
• 2013.10.10.w6.th-lecture-11-generalization.pdf
• 2013.10.22.w8.tu-lecture-13-generalization-part-2.pdf
• 2013.10.24.w8.th-lecture-14-generalization-part-3.pdf
• 2013.10.29.w9.tu-lecture-15-gen-modelling-gaussian-estimation.pdf
• 2013.10.31.w9.th-lecture-16-expectation-maximization.pdf
• 2013.11.05.w10.tu-lecture-17-document-topic-modelling.pdf
• 2013.11.07.w10.th-lecture-18-more-document-topic-modelling-and-lda.pdf
• 2013.11.12.w11.tu-lecture-19-graphs-independence-dirichlet-processes.pdf
• 2013.11.14.w11.th-lecture-20-dirichlet-processes.pdf
• 2013.11.19.w12.tu-lecture-21-hidden-markov-models.pdf
• 2013.11.21.w12.th-lecture-22-more-hmm.pdf
• 2013.11.26.w13.tu-lecture-23-bayesian-network-inference.pdf
• 2013.12.03.w14.tu-lecture-24-more-bayesian-networks.pdf
• 2013.12.05.w14.th-lecture-25-reinforcement-learning.pdf