Physics-Informed Neural Networks (PINNs) are an attractive tool for partial-observation problems in biology, where the governing dynamics are known but some compartments cannot be measured. Chemotherapy pharmacokinetics (PK) is a clean instance: drug concentration in plasma is routinely measured, but concentration in tissue -- which determines tumour kill and off-target toxicity -- is not. We benchmark a PINN against the standard clinical baseline (nonlinear least-squares on the analytical biexponential plasma solution, hereafter NLS) and a physics-agnostic neural baseline (a data-only MLP) on two PK problems. On the linear two-compartment problem, NLS is near-optimal; the PINN matches it to within a small constant factor while also producing the tissue curve in a single training pass, whereas the data-only MLP fails on tissue by roughly 10x. On a Michaelis-Menten extension (saturable elimination), the biexponential closed form no longer exists, so NLS is mis-specified and silently returns meaningless rate constants. The PINN instead exposes a deeper fact: the Michaelis-Menten two-compartment model is non-identifiable from plasma alone, and the PINN reports this honestly by converging to a basin with k12 -> 0. Adding two sparse tissue observations largely resolves identifiability: across five seeds the PINN recovers k21 to within 1% of truth and Vmax, Km to within one standard-deviation bar, while k12 moves in the correct direction (0.02 -> 0.82) but remains ~2 sigma below truth -- a recovery the closed-form NLS estimator cannot attempt at all, because its biexponential ansatz describes only plasma. Our claim is not that PINNs beat NLS. It is that PINNs offer a uniform recipe that ties the textbook estimator on the textbook problem, exposes structural identifiability that the textbook estimator hides, and absorbs heterogeneous measurements within a single loss.

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at https://physics.bu.edu/~pankajm/MLnotebooks.html )

The eight sections are (1) Artificial Intelligence (introductory material); (2) Problem-Solving (search and game playing); (3) Knowledge and Reasoning (propositional and predicate logic, inference techniques, knowledge representation); (4) Acting Logically (planning); (5) Uncertain Knowledge and Reasoning (probabilistic reasoning, Bayesian nets, decision-theoretic techniques); (6) Learning (inductive learning, neural nets, reinforcement learning); (7) Communicating, Perceiving, and Acting (natural language processing, computer vision, robotics); and (8) Conclusions (philosophical foundations and summary). What makes this textbook so good? First, it is remarkably comprehensive. In the preface, the authors suggest several alternative paths through the book that could serve as the basis of a one-semester course. At the University of Pittsburgh, my colleagues and I cover roughly the first half of the book (Sections 1-4) in the firstsemester introductory graduate AI course, covering most of Sections 5 through 8 in a second-semester course.

Statistical learning and related methods are necessary to work in data science. This textbook is designed to help anyone and everyone, from an undergraduate to a Ph.D. student, understand the concepts. Of course, it also offers a great selection of R labs and practices, with detailed explanations and walkthroughs. The idea is that you can use it as a direct resource while practicing data science, especially during the educational phase. Plus, it's a great resource to have around and look back at regularly. The concepts and information are practical for daily applications.

"This book appears in the Information Science and Statistics Series commissioned by the publishers.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Prerequisites: This is an advanced course, meant for upper-level undergraduates and beginning graduate students, but it is open to anyone. Good familiarity with algorithmic concepts is necessary. We will try to recall the concepts needed along the way. However, having taken a course in algorithms such as CS161 is particularly useful, otherwise it may be tough to follow the material. There is no textbook for the course, but we will catalogue some reading material found on the web as we go, and we will have lecture notes.

I would like to add on to the post. Image processing is a field that has existed on its own longer than machine learning (ie, it predates machine learning decades before), its been taught mainly as a branch of engineering (electrical & electronics) & to some lesser degree also taught in computer science & physics' courses. Its only in the last decade or so, that image processing includes machine learning topics' for image recognition & understanding. The latest edition (3rd) has an added chapter on "Object Recognition" which wasn't available in the 1st & 2nd edition. The last time I passed through my local university bookstore (about a year ago), this textbook is stocked because its still currently a prescribed textbook for final year Electrical engineering courses.

There is much debate among scholars and practitioners about what data science is, and what it isn't. Does it deal only with big data? Is data science really that new? How is it different from statistics and analytics? One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.

Our fourth installment of Research for Practice covers two of the hottest topics in computer science research and practice: cryptocurrencies and deep learning. First, Arvind Narayanan and Andrew Miller, co-authors of the increasingly popular open access Bitcoin textbook, provide an overview of ongoing research in cryptocurrencies. This is a topic with a long history in the academic literature that has recently come to prominence with the rise of Bitcoin, blockchains, and similar implementations of advanced, decentralized protocols. These developments--and colorful exploits such as the DAO vulnerability in June 2016--have captured the public imagination and the eye of the popular press. In the meantime, academics have been busy, delivering new results in maintaining anonymity, ensuring usability, detecting errors, and reasoning about decentralized markets, all through the lens of these modern cryptocurrency systems.