We are a group of students from IIITD pursuing Masters in CSE. In this article, We have proposed a comparative analysis between Deep Leaning techniques and ML Algorithms to solve a computer vision Kaggle challenge. Driving a car is a complex task, and it requires complete attention. Distracted driving is any activity that takes away the driver's attention from the road. Approximately 1.35 million people die each year as a result of road traffic crashes.

Learn Machine Learning from scratch, this course for beginner who want to learn the fundamental of machine learning and artificial intelligence.

The fluctuation effect of gradient expectation and variance caused by parameter update between consecutive iterations is neglected or confusing by current mainstream gradient optimization algorithms.Using this fluctuation effect, combined with the stratified sampling strategy, this paper designs a novel \underline{M}emory \underline{S}tochastic s\underline{T}ratified Gradient Descend(\underline{MST}GD) algorithm with an exponential convergence rate. Specifically, MSTGD uses two strategies for variance reduction: the first strategy is to perform variance reduction according to the proportion p of used historical gradient, which is estimated from the mean and variance of sample gradients before and after iteration, and the other strategy is stratified sampling by category. The statistic \ $\bar{G}_{mst}$\ designed under these two strategies can be adaptively unbiased, and its variance decays at a geometric rate. This enables MSTGD based on $\bar{G}_{mst}$ to obtain an exponential convergence rate of the form $\lambda^{2(k-k_0)}$($\lambda\in (0,1)$,k is the number of iteration steps,$\lambda$ is a variable related to proportion p).Unlike most other algorithms that claim to achieve an exponential convergence rate, the convergence rate is independent of parameters such as dataset size N, batch size n, etc., and can be achieved at a constant step size.Theoretical and experimental results show the effectiveness of MSTGD

Research is needed to explore the limitations and potential for improvement of machine learning for building energy prediction. With this aim, the ASHRAE Great Energy Predictor III (GEPIII) Kaggle competition was launched in 2019. This effort was the largest building energy meter machine learning competition of its kind, with 4,370 participants who submitted 39,403 predictions. The test data set included two years of hourly whole building readings from 2,380 meters in 1,448 buildings at 16 locations. This paper analyzes the various sources and types of residual model error from an aggregation of the competition's top 50 solutions. This analysis reveals the limitations for machine learning using the standard model inputs of historical meter, weather, and basic building metadata. The errors are classified according to timeframe, behavior, magnitude, and incidence in single buildings or across a campus. The results show machine learning models have errors within a range of acceptability (RMSLE_scaled =< 0.1) on 79.1% of the test data. Lower magnitude (in-range) model errors (0.1 < RMSLE_scaled =< 0.3) occur in 16.1% of the test data. These errors could be remedied using innovative training data from onsite and web-based sources. Higher magnitude (out-of-range) errors (RMSLE_scaled > 0.3) occur in 4.8% of the test data and are unlikely to be accurately predicted.

Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian backfitting algorithm used to fit BART models, however, limits their application to a small class of models for which conditional conjugacy exists. In this article, we greatly expand the domain of applicability of BART to arbitrary \emph{generalized BART} models by introducing a very simple, tuning-parameter-free, reversible jump Markov chain Monte Carlo algorithm. Our algorithm requires only that the user be able to compute the likelihood and (optionally) its gradient and Fisher information. The potential applications are very broad; we consider examples in survival analysis, structured heteroskedastic regression, and gamma shape regression.

We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD can be theoretically more efficient than a single-chain CSGLD with an equivalent computational budget. We also present a novel random-field function, which facilitates the estimation of self-adapting parameters in big data and obtains free mode explorations. Empirically, we compare the proposed algorithm with popular benchmark methods for posterior sampling. The numerical results show a great potential of ICSGLD for large-scale uncertainty estimation tasks.

Evaluation metrics to analyze the performance of models Industry relevance of linear and logistic regression Mathematics behind KNN, SVM and Naive Bayes algorithms Implementation of KNN, SVM and Naive Bayes using sklearn Attribute selection methods- Gini Index and Entropy Mathematics behind Decision trees and random forest Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost Different Algorithms for Clustering Different methods to deal with imbalanced data Correlation Filtering Content and Collaborative based filtering Singular Value Decomposition Different algorithms used for Time Series forecasting Hands on Real-World examples. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning.

We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. The current state-of-the-art uncertainty algorithms either fall short of achieving prediction accuracy comparable to the mean square error optimization or underestimate the variance of network predictions. We propose a decoupled network architecture that is capable of accomplishing both at the same time. We achieve this by breaking down the learning of prediction and prediction interval (PI) estimations into a two-stage training process. We use a custom loss function for learning a PI range around optimized mean estimation with a desired coverage of a proportion of the target labels within the PI range. We compare the proposed method with current state-of-the-art uncertainty quantification algorithms on synthetic datasets and UCI benchmarks, reducing the error in the predictions by 23 to 34% while maintaining 95% Prediction Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. We also examine the quality of our predictive uncertainty by evaluating on Active Learning and demonstrating 17 to 36% error reduction on UCI benchmarks.

Ever since the introduction of Differential Privacy (DP) by Dwork et al. (2006), differentially private variants of statistical estimation procedures have been a research topic of intense interest. The work on learning linear models alone is vast (see Cai et al. (2020); Wang (2018) for two recent reviews). Empirical Risk Minimization is also the impetus for the development of a broad array of new methods for DP-mechanism design, including output perturbation (Iyengar et al., 2019; Zhang et al., 2017; Jain and Thakurta, 2014), objective perturbation (Chaudhuri et al., 2011; Kifer et al., 2012), and gradient perturbation (Bassily et al., 2014; Abadi et al., 2016), to name a few. Nevertheless, despite the intense interest on this topic, all of the existing work on regression provides differential-privacy guarantees assuming bounded covariates. Intuitively, this can be explained by inspecting even the simple least squares estimator used in linear regression. It is easy to see that estimator's sensitivity, i.e., its variability under changes on a single sample, is determined by the design matrix (i.e., the matrix of samples). As sensitivity has a direct effect on differential privacy guarantees, bounding the design matrix's eigenvalues is the prevalent approach for bounding the sensitivity. For this reason, assuming bounded covariates is a ubiquitous assumption in DP literature on both linear regression and learning generalized linear models.

A machine learning model may have several features, but some feature might have a higher impact on the output than others. For example, if a model is predicting apartment prices, the locality of the apartment might have a higher impact on the output than the number of floors the apartment building has. Hence, we come up with the concept of weights. Each feature is associated with a weight (a number) i.e. the higher the feature has an impact on the output, the larger the weight associated with it. But how do you decide what weight should be assigned to each feature?