hangtaiKeyLhortnyofMhkeorCa咖ssandhmoratrceMatrik,Deoat血etofhsamiy 机器学习的类型 Supervised Learning(监督学习) The computer is presented with example inputs and their desired outputs, given by a"teacher",and the goal is to learn a general rule that maps inputs to outputs. Semi-supervised learning:the computer is given only an incomplete training signal:a training set with some(often many)of the target outputs missing. Active learning:the computer can only obtain training labels for a limited set of instances(based on a budget),and also has to optimize its choice of objects to acquire labels for.When used interactively,these can be presented to the user for labeling. Reinforcement learning:training data(in form of rewards and punishments)is given only as feedback to the program's actions in a dynamic environment,such as driving a vehicle or playing a game against an opponent. 振华制 数理统计在化学中的应用 6 造
李 振 华 制 造 机器学习的类型 Supervised Learning （监督学习） The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Semi-supervised learning: the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing. Active learning: the computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling. Reinforcement learning: training data (in form of rewards and punishments) is given only as feedback to the program's actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent. 数理统计在化学中的应用 6
KLao fMoaymovive Deparm of Ch Starting point: Response measurement Y Vector of p predictor measurements X In the regression problem,Y is quantitative (e.g.price, blood pressure). ● In the classification problem,Y takes values in a finite, unordered set(survived/died,digit 0-9,cancer class of tissue sample). We have training data (x,);...;(x y).These are observations (examples,instances)of these measurements. 振华制 数理统计在化学中的应用
李 振 华 制 数理统计在化学中的应用 7 造 Starting point: Response measurement Y Vector of p predictor measurements X In the regression problem, Y is quantitative (e.g. price, blood pressure). In the classification problem, Y takes values in a finite, unordered set (survived/died, digit 0-9, cancer class of tissue sample). We have training data (x1 , y1 ); … ; (xN , yN ). These are observations (examples, instances) of these measurements
UN KeyyofMsivDeprment of Ch 监督学 Labels already Training: KNOWN Feature Feature Feature Feature Known #1 #2 #3 . N Labels Build model 李振华制 数理统计在化学中的应用 8
李 振 华 制 造 监督学习 数理统计在化学中的应用 8 Feature #1 Feature #2 Feature #3 … Feature N Build model Known Labels Labels already KNOWN Training:
UN KeyyofMivDeprment of Ch 91 监督学 Labels NOT Training: KNOWN Feature Feature Feature Feature Goal #1 #2 #3 。o N Labels Use model built during training 数理统计在化学中的应用 9 李振华制造
李 振 华 制 造 监督学习 数理统计在化学中的应用 9 Feature #1 Feature #2 Feature #3 … Feature N Use model built during training Goal Labels Labels NOT KNOWN Training:
Key La fMoeiveDprmentof Ch 机器学习的类型 Unsupervised Learning(无监督学习 No labels are given to the learning algorithm,leaving it on its own to find structure in its input.Unsupervised learning can be a goal in itself (discovering hidden patterns in data)or a means towards an end (feature learning). No outcome variable,just a set of predictors(features)measured on a set of samples. objective is more fuzzy find groups of samples that behave similarly find features that behave similarly Find linear combinations of features with the most variation. difficult to know how well your are doing different from supervised learning,but can be useful as a pre- processing step for supervised learning 振华制 数理统计在化学中的应用 10 造
李 振 华 制 造 机器学习的类型 Unsupervised Learning （无监督学习） No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). No outcome variable, just a set of predictors (features) measured on a set of samples. objective is more fuzzy find groups of samples that behave similarly find features that behave similarly Find linear combinations of features with the most variation. difficult to know how well your are doing different from supervised learning, but can be useful as a preprocessing step for supervised learning 数理统计在化学中的应用 10