Previous post: ML theory with bad drawings Next post: TBD, see also all seminar posts and course webpage. Lecture video (starts in slide 2 since I hit record button 30 seconds too late – sorry!) These are rough notes for the first lecture in my advanced topics in machine learning seminar. See the previous post for the introduction. This lecture's focus was on "classical" learing theory.

After two introductory tutorials, its time to build our first neural network! The network we are building solves a simple regression problem. Regression is a process where a model learns to predict a continuous value output for a given input data, e.g. Our objective is to build prediction model that predicts housing prices from a set of house features. We will use the Boston Housing dataset, which is collected by the U.S Census Service concerning housing in the area of Boston Mass.

With large-scale integration of renewable generation and ubiquitous distributed energy resources (DERs), modern power systems confront a series of new challenges in operation and control, such as growing complexity, increasing uncertainty, and aggravating volatility. While the upside is that more and more data are available owing to the widely-deployed smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. In this paper, we focus on RL and aim to provide a tutorial on various RL techniques and how they can be applied to the decision-making and control in power systems. In particular, we select three key applications, including frequency regulation, voltage control, and energy management, for illustration, and present the typical ways to model and tackle them with RL methods. We conclude by emphasizing two critical issues in the application of RL, i.e., safety and scalability. Several potential future directions are discussed as well.

In many applications of machine learning, such as recommender systems, where an input characterizing a user should be matched with a target representing an ordering of a large number of items, accessing fully supervised data (,) is not an option. Instead, one should expect weak information on the target, which could be a list of previously taken (if items are online courses), watched (if items are plays), etc., items by a user characterized by the feature vector. This motivates weakly supervised learning, aiming at learning a mapping from inputs to targets in such a setting where tools from supervised learning can not be applied off-the-shelves. Recent applications of weakly supervised learning showcase impressive results in solving complex tasks such as action retrieval on instructional videos (Miech et al., 2019), image semantic segmentation (Papandreou et al., 2015), salient object detection (Wang et al., 2017), 3D pose estimation (Dabral et al., 2018), text-to-speech synthesis (Jia et al., 2018), to name a few. However, those applications of weakly supervised learning are usually based on clever heuristics, and theoretical foundations of learning from weakly supervised data are scarce, especially when compared to statistical learning literature on supervised learning (Vapnik, 1995; Boucheron et al., 2005; Steinwart and Christmann, 2008). We aim to provide a step in this direction. In this paper, we focus on partial labelling, a popular instance of weak supervision, approached with a structured prediction point of view Ciliberto et al. (2020). We detail this setup in Section 2. Our contributions are organized as follows.

Despite interest in communicating ethical problems and social contexts within the undergraduate curriculum to advance Public Interest Technology (PIT) goals, interventions at the graduate level remain largely unexplored. This may be due to the conflicting ways through which distinct Artificial Intelligence (AI) research tracks conceive of their interface with social contexts. In this paper we track the historical emergence of sociotechnical inquiry in three distinct subfields of AI research: AI Safety, Fair Machine Learning (Fair ML) and Human-in-the-Loop (HIL) Autonomy. We show that for each subfield, perceptions of PIT stem from the particular dangers faced by past integration of technical systems within a normative social order. We further interrogate how these histories dictate the response of each subfield to conceptual traps, as defined in the Science and Technology Studies literature. Finally, through a comparative analysis of these currently siloed fields, we present a roadmap for a unified approach to sociotechnical graduate pedagogy in AI.

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Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the ideas and perspectives can seem alien to statisticians. Nonetheless, it is important that statisticians become involved -- many of our students need this expertise for their careers. In this paper, developed as part of a program on DL held at the Statistical and Applied Mathematical Sciences Institute, we address this culture gap and provide tips on how to teach deep learning to statistics graduate students. After some background, we list ways in which DL and statistical perspectives differ, provide a recommended syllabus that evolved from teaching two iterations of a DL graduate course, offer examples of suggested homework assignments, give an annotated list of teaching resources, and discuss DL in the context of two research areas.

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Technical growths have empowered, numerous revolutions in the educational system by acquainting with technology into the classroom and by elevating the learning experience. Nowadays Web-based learning is getting much popularity. This paper describes the web-based learning and their effectiveness towards students. One of the prime factors in education or learning system is feedback; it is beneficial to learning if it must be used effectively. In this paper, we worked on how machine learning techniques like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) can be applied over Web-based learning, emphasis given on sentiment present in the feedback students. We also work on two types of Feature Extraction Technique (FETs) namely Count Vector (CVr) or Bag of Words) (BoW) and Term Frequency and Inverse Document Frequency (TF-IDF) Vector. In the research study, it is our goal for our proposed LR, SVM, NB, and DT models to classify the presence of Student Feedback Dataset (SFB) with improved accuracy with cleaned dataset and feature extraction techniques. The SFB is one of the significant concerns among the student sentimental analysis.

This idea was further extended in (Xie et al., 2017), where Symmetric positive definite (SPD) matrices are widely used sub-manifold learning for dimension reduction is used before data structures in many disciplines, e.g. in medical imaging the tangent space approximation. However, the first-order approximations (Penne et al., 2006) and computer vision as covariance region can lead to undesirable distortion, especially in descriptors (Tuzel et al., 2006; Jayasumana et al., 2015), regions far from the tangent space origin (Tuzel et al., 2008; as well as in brain-computer interface (BCI) (Congedo et al., Jayasumana et al., 2015). The mean of the SPD matrices is a 2017), etc. Endowed with an appropriate metric, SPD matrices frequently used candidate for the tangent space origin, however, form a curved Riemannian manifold. Consequently, many popular no theoretical proof exists to guarantee the mean yields the best machine learning algorithms such as linear discriminant tangent space approximation for the data (Tuzel et al., 2008).