# Machine Learning Stanford Homework

Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

## machine learning stanford homework

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This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering,dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.Students are expected to have the following background:Prerequisites:- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)

Ng's research is in the areas of machine learning and artificial intelligence. He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI.Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. Using this approach, Ng's group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute. As part of this work, Ng's group also developed algorithms that can take a single image,and turn the picture into a 3-D model that one can fly-through and see from different angles.

The course will include in-person lectures (also livestreamed and recorded over Zoom), fourgraded homework assignments, one optional homework assignment, and a course project. The lectures willdiscuss the fundamentals of topics required for understanding anddesigning multi-task and meta-learning algorithms in various domains. The assignments will focus on coding problems that emphasizethese fundamentals. Finally, students will present their projects at the poster session atthe end of the quarter.

Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.

Proficiency in Python.Homework assignments will be in a mixture of Python using PyTorch, Jupyter Notebooks, Amazon Skills Kit, and other tools.We attempt to make the course accessible to students with a basic programmingbackground, but ideally students will have some experience with machinelearning or natural language tasks in Python.

Foundations of Machine Learning and Natural Language Processing (CS 124, CS 129, CS 221, CS 224N, CS 229 or equivalent).You should be comfortable with basic concepts of machine learning and natural language processing. We do not strictly enforce a particular set of previous courses but students will have to fill in gaps on their own depending on background.

Fundamental concepts and theories in machine learning, supervised and unsupervised learning, regression and classification, loss function selection and its effect on learning, regularization and robustness to outliers, numerical experiments on data from a wide variety of engineering and other disciplines.

Concentrates on recognizing and solving convex optimization problems that arise in applications. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing,statistics and machine learning, control and mechanical engineering,digital and analog circuit design, and finance.

This course should benefit anyone who uses or will use scientific computing or optimization in engineering or related work (e.g., machine learning, finance). More specifically, peoplefrom the following departments and fields: Electrical Engineering (especially areas like signal and image processing, communications, control, EDA & CAD);Aero & Astro (control, navigation, design),Mechanical & Civil Engineering (especially robotics, control, structural analysis, optimization, design);Computer Science(especially machine learning, robotics, computer graphics, algorithms & complexity, computational geometry);Operations Research(MS&E at Stanford); Scientific Computingand Computational Mathematics. The course may be useful to students and researchers in several other fields as well: Mathematics, Statistics, Finance, Economics.

Probabilistic graphical models are a powerful framework for representing complex domains using probability distributions, with numerous applications in machine learning, computer vision, natural language processing and computational biology. Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. This course will provide a comprehensive survey of the topic, introducing the key formalisms and main techniques used to construct them, make predictions, and support decision-making under uncertainty.

Given a dataset of student code, we can ask aninstructor to provide feedback for each of the solutions, creating alabeled dataset. This can be used to train a deep learning model topredict feedback for a new student solution. While this is great intheory, in practice, compiling a sufficiently large and diverse datasetis difficult. In machine learning, we are accustomed to datasets withmillions of labeled examples since annotating an image is both cheap andrequires no domain knowledge. On the other hand, annotating studentcode with feedback is both time-consuming and needs expertise, limitingdatasets to be a few thousand examples in size. Given the Zipf-likenature of student code, it is very unlikely that a dataset of this sizecan capture all the different ways students approach a problem. This isreflected in practice as supervised attempts perform poorly on newstudent solutions.

This is a project-based class where students will learn how to develop machine learning models for execution in resource constrained environments such as embedded systems. The primary target is embedded devices such as Arduino, Raspberry PI, Jetson, or Edge TPUs.

Sylabus: Machine learning has received enormous interest. To learn from data we use probability theory, which has been the mainstay of statistics and engineering for centuries. The class will focus on implementations for physical problems. Topics: Gaussian probabilities, linear models for regression, linear models for classification, neural networks, kernel methods, support vector machines, graphical models, mixture models, sampling methods, sequential estimation. Prerequisites: graduate standing.

It is not a computer science class, so we go slowly through the fundamentalsto appreciate the methods, implement these. We will discuss their use inPhysical Sciences. In the first part of the class we focus on theory and implementations. Wewill then transition to focus on machine learning final projects.