hat
Kou sa a kouvri yon varyete paradigms aprantisaj, algoritm, rezilta teyorik ak aplikasyon. Li itilize konsèp fondamantal nan entèlijans atifisyèl, teyori enfòmasyon, estatistik, ak teyori kontwòl sof si yo gen rapò ak aprantisaj machin. Sijè yo enkli: aprantisaj sipèvize (aprantisaj jeneratif / diskriminatwa, aprantisaj parametric / non-parametric, rezo neral, machin vektè sipò, pye desizyon, aprantisaj ak optimize Bayesian); aprantisaj san sipèvizyon (clustering, rediksyon dimansyon, metòd du); teyori aprantisaj (patipri / divèjans komès; teyori VC; gwo marges); aprantisaj ranfòsman ak kontwòl adaptasyon. Lòt sijè yo enkli HMM (Hidden Markov ki gen konpòtman egzanplè), Computational evolisyonè, Aprantisaj fon (ak nè nè) ak algoritm konsepsyon ki gen pèfòmans yo ka egzamine règleman pou pwoblèm aprantisaj machin fondamantal.
eng
This course covers a variety of learning paradigms, algorithms, theoretical results and applications. It uses basic concepts from artificial intelligence, information theory, statistics, and control theory insofar as they are relevant to machine learning. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines, decision tree, Bayesian learning & optimization); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. Other topics include HMM (Hidden Markov Model), Evolutionary Computing, Deep Learning (With Neural Nets) and designing algorithms whose performance can be rigorously analyzed for fundamental machine learning problems.