The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios. UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data.

The accuracy of the estimated stellar atmospheric parameter decreases evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there are a huge amount of this kind observations, especially in case of SNR$<$30. Therefore, it is helpful to improve the parameter estimation performance for these spectra and this work studied the ($T_\texttt{eff}, \log~g$, [Fe/H]) estimation problem for LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30. We proposed a data-driven method based on machine learning techniques. Firstly, this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator (LASSO), rejected ineffective data components and irrelevant data. Secondly, a Multi-layer Perceptron (MLP) method was used to estimate stellar atmospheric parameters from the LASSO features. Finally, the performance of the LASSO-MLP was evaluated by computing and analyzing the consistency between its estimation and the reference from the APOGEE (Apache Point Observatory Galactic Evolution Experiment) high-resolution spectra. Experiments show that the Mean Absolute Errors (MAE) of $T_\texttt{eff}, \log~g$, [Fe/H] are reduced from the LASP (137.6 K, 0.195 dex, 0.091 dex) to LASSO-MLP (84.32 K, 0.137 dex, 0.063 dex), which indicate evident improvements on stellar atmospheric parameter estimation. In addition, this work estimated the stellar atmospheric parameters for 1,162,760 low-resolution spectra with 20$\leq$SNR$<$30 from LAMOST DR8 using LASSO-MLP, and released the estimation catalog, learned model, experimental code, trained model, training data and test data for scientific exploration and algorithm study.

This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that are corrupted with non-Gaussian noise or outliers, which are commonly found in engineering applications. In particular, it combines recursive, alternating, and iteratively-reweighted least squares into a single, one-step algorithm, which solves the estimation problem online and benefits from the robustness of least-deviation regression methods. The convergence of the iterative method is formally guaranteed. Numerical experiments show the good performance of the estimation algorithm in presence of outliers and in comparison to state-of-the-art methods.

The R-square is a measure of how well the linear regression model fits the observed data. It is calculated by squaring the correlation coefficient and dividing by the standard deviation of errors. It is the square of the correlation coefficient divided by its standard deviation (r2/s2). The R-square value of 1 indicates that the model explains 100% of the variation in Y. The R-square values greater than 1 indicate that the model explains more than 100% of the variation in Y.

Evaluation metrics to analyze the performance of models. Different methods to deal with imbalanced data. Implementation of Content and Collaborative based filtering. Implementation of Different algorithms used for Time Series forecasting. Evaluation metrics to analyze the performance of models.

'The quantile function is a mathematical function that takes a quantile (a percentage of a distribution ranging from 0 to 1) as an input and returns the value of a variable as an output.' It can answer queries such as, "How much inventory do I need to maintain on hand if I want to guarantee that 95 percent of my customers receive their orders within 24 hours?" As a result, the quantile function is frequently utilized in forecasting questions. However, in practice, there is rarely a neat method for computing the quantile function. That means that if you want to compute it for a different quantile, you'll need to create a new regression model, which nowadays usually entails retraining a neural network.

線形回帰は、単純線形回帰とも呼ばれ、従属変数と1つの独立変数の間の関係をモデル化する回帰アルゴリズムです。線形回帰モデルは、線形または傾斜した直線である関係を示すので、単純線形回帰という名前になりました。

Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representations driven by a similarity measure between a query image and few support images of some class. Statistically, this amounts to measure the dependency of image features, viewed as random vectors in a high-dimensional embedding space. Previous methods either only use marginal distributions without considering joint distributions, suffering from limited representation capability, or are computationally expensive though harnessing joint distributions. In this paper, we propose a deep Brownian Distance Covariance (DeepBDC) method for few-shot classification. The central idea of DeepBDC is to learn image representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. As the BDC metric is decoupled, we formulate it as a highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two different few-shot classification frameworks. We make experiments on six standard few-shot image benchmarks, covering general object recognition, fine-grained categorization and cross-domain classification. Extensive evaluations show our DeepBDC significantly outperforms the counterparts, while establishing new state-of-the-art results. The source code is available at http://www.peihuali.org/DeepBDC

The ability to analyze single cells is one of the holy grails of precision medicine. Yuri Belotti, PhD, Doorgesh Sharma Jokhun, PhD, and Professor Chwee Teck (C.T.) Lim at National University of Singapore have developed a novel protocol for single-cell classification based on intracellular pH. Their paper entitled Machine learning based approach to pH imaging and classification of single cancer cells was published in APL Bioengineering. The pH in the human the body varies between 4.7 and 8.0. Cancer growth, metastasis, and other diseases including Alzheimer's have been linked to deviations from normal intracellular acidity.

In this paper, we present a study of a kernel-based consensual aggregation on randomly projected high-dimensional features of predictions for regression. The aggregation scheme is composed of two steps: the high-dimensional features of predictions, given by a large number of regression estimators, are randomly projected into a smaller subspace using Johnson-Lindenstrauss Lemma in the first step, and a kernel-based consensual aggregation is implemented on the projected features in the second step. We theoretically show that the performance of the aggregation scheme is close to the performance of the aggregation implemented on the original high-dimensional features, with high probability. Moreover, we numerically illustrate that the aggregation scheme upholds its performance on very large and highly correlated features of predictions given by different types of machines. The aggregation scheme allows us to flexibly merge a large number of redundant machines, plainly constructed without model selection or cross-validation. The efficiency of the proposed method is illustrated through several experiments evaluated on different types of synthetic and real datasets.