Tom Mitchell Machine Learning Solution Manual.zip PORTABLE
Download File ->->->-> https://fancli.com/2sZmW5
During the first part of the semester we will cover general knowledge representation techniques and problem solving strategies. Topics will includesemantic nets,search, intelligent agents,game playing,constraint satisfaction,rule-based systems,logic-based systems,logic programming,planning, reasoning with uncertainty,and probabilistic reasoning.During the second part of the semester we will discuss three importantapplication areas in AI: machine learning, naturallanguage processing, and machine vision. For the catalog description of this course see the WPI Graduate Catalog. CLASS MEETING: Tuesdays and Thursdays 3:30-4:50 pm AK232Students are also encouraged to attend the AIRG Seminar Thursdays at 11 am and the KDDRG Seminar Fridays at 2 pm. INSTRUCTOR: Prof. Carolina Ruiz Office: FL 232 Phone Number: Office Hours: Thursdays 1-2 pm, or by appointment. TEXTBOOK: Stuart Russell and Peter Norvig. "Artificial Intelligence: A Modern Approach". Second Edition.Prentice Hall, 2003. RECOMMENDED BACKGROUND: Familiarity with data structures and a recursive high-level language. GRADES: Exam 1 20% Exam 2 20% Project 25% Homework 35% Class Participation Extra Points Your final grade will reflect your own work and achievementsduring the course. Any type of cheating will be penalized in accordance to the Academic Honesty Policy.Students are expected to read the material assigned to eachclass in advance and to participate in class. Class participation will be taken into account when deciding students' final grades. EXAMS There will be a total of 2 exams. Each exam will cover thematerial presented in class since the beginning of the semester.In particular, the final exam is cumulative.The midterm exam is scheduled for October 11, 2007 and the final exam is scheduled for December 11, 2007. HOMEWORK AND PROJECSHomeworkThere will be several, individual homework assignments during the semester.The homework statements will be posted on the course webpage.Generally homework solutions are due on Thursdays. Each student should hand-in his/her own individual written homework solutions at the beginning of the class when the homework is due, and should be prepared to present and discusshis/her homework solutions in class immediatly after.ProjectThere will be one major course project. This project may consist of severalsmaller parts.A detailed description of the project will be posted to the course webpageat the appropriate time during the semester.Although you may find similar programs/systems available online or in the references, the design and all code you use and submit for you projects MUST be your own original work. CLASS MAILING LIST The mailing list for this class is: This mailing list reaches the professor and all the students in the class. CLASS WEB PAGES The web pages for this class are located at ~cs534/f07/Announcements will be posted on the web pages and/or the class mailing list, and so you are urged to check your email andthe class web pages frequently. ADDITIONAL SUGGESTED REFERENCES General AI The following additional references complement and/or supplement the material contained in the required textbook. I have listedthem in decreasing order of interest according to my own preferences.T. Dean, J. Allen, Y. Aloimonos."Artificial Intelligence: Theory and Practice"The Benjamin/Cummings Publishing Company, Inc. 1995. B. L. Webber, N. J. Nilsson, eds. "Readings in Artificial Intelligence"Tioga Publishing Company, 1981.
COMP 4211. Fundamentals of machine learning. Conceptlearning. Evaluating hypotheses. Supervised learning, unsupervised learning andreinforcement learning. Bayesian learning. Ensemble Methods. Exclusion(s): COMP4331, ISOM 3360 Prerequisite(s): COMP 171/171H (prior to 2009-10) or COMP2012/2012H, and MATH 2411/2421/246.
C.L. andQ.G. supervised the project. B.B.H., C.C., and C.L. developed thetheoretical computational frameworks for the generation of simulatedcyclic voltammograms. B.B.H. generated and sanitized the data necessaryfor model training/validation. Q.G. and W.Z. established the machine-learningmodel and provided codes for data analysis to B.B.H. and C.L. S.X.synthesized the desirable compounds for electrochemical testing. S.X.,R.D., and C.C. provided the experimental data of cyclic voltammetry.B.B.H. prepared the initial draft of the manuscript. All of the authorsdiscussed the results of the project and assisted with manuscriptpreparation. CRediT: Benjamin B Hoar conceptualization(equal), data curation (lead), formal analysis (lead), investigation(lead), software (equal), validation (equal), visualization (equal),writing-original draft (lead), writing-review & editing (lead); Weitong Zhang data curation (equal), methodology (equal),software (equal), writing-review & editing (equal); ShuangningXu data curation (equal), methodology (equal), writing-review& editing (equal); Rana Deeba data curation, methodology,resources (equal); Cyrille Costentin data curation, formalanalysis (equal), investigation (equal), validation, writing-originaldraft (equal), writing-review & editing; Quanquan Gu formal analysis (equal), investigation (equal), resources (equal),writing-original draft (equal), writing-review & editing (equal); Chong Liu conceptualization (lead), formal analysis (equal),funding acquisition (equal), investigation (equal), project administration(equal), resources (equal), supervision (lead), writing-original draft(equal), writing-review & editing (lead).
When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than traditional server-grade hardware and are often battery powered, severely limiting the sophistication of models that can be trained under this paradigm. While most research has focused on designing better aggregation strategies to improve convergence rates and in alleviating the communication costs of FL, fewer efforts have been devoted to accelerating on-device training. Such stage, which repeats hundreds of times (i.e. every round) and can involve thousands of devices, accounts for the majority of the time required to train federated models and, the totality of the energy consumption at the client side. In this work, we present the first study on the unique aspects that arise when introducing sparsity at training time in FL workloads. We then propose ZeroFL, a framework that relies on highly sparse operations to accelerate on-device training. Models trained with ZeroFL and 95% sparsity achieve up to 2.3% higher accuracy compared to competitive baselines obtained from adapting a state-of-the-art sparse training framework to the FL setting.
Mutual information (MI) is a fundamental quantity in information theory and machine learning. However, direct estimation of mutual information is intractable, even if the true joint probability density for the variables of interest is known, as it involves estimating a potentially high-dimensional log partition function. In this work, we view mutual information estimation from the perspective of importance sampling. Since naive importance sampling with the marginal distribution as a proposal requires exponential sample complexity in the true mutual information, we propose several improved proposals which assume additional density information is available. In settings where the full joint distribution is available, we propose Multi-Sample Annealed Importance Sampling (AIS) bounds on mutual information, which we demonstrate can tightly estimate large values of MI in our experiments. In settings where only a single marginal distribution is known, our MINE-AIS method improves upon existing variational methods by directly optimizing a tighter lower bound on MI, using energy-based training to estimate gradients and Multi-Sample AIS for evaluation. Our methods are particularly suitable for evaluating MI in deep generative models, since explicit forms for the marginal or joint densities are often available. We evaluate our bounds on estimating the MI of VAEs and GANs trained on the MNIST and CIFAR datasets, and showcase significant gains over existing bounds in these challenging settings with high ground truth MI.
One potential drawback of using aggregated performance measurement in machine learning is that models may learn to accept higher errors on some training cases as compromises for lower errors on others, with the lower errors actually being instances of overfitting. This can lead both to stagnation at local optima and to poor generalization. Lexicase selection is an uncompromising method developed in evolutionary computation, which selects models on the basis of sequences of individual training case errors instead of using aggregated metrics such as loss and accuracy. In this paper, we investigate how the general idea of lexicase selection can fit into the context of deep learning to improve generalization. We propose Gradient Lexicase Selection, an optimization framework that combines gradient descent and lexicase selection in an evolutionary fashion. Experimental results show that the proposed method improves the generalization performance of various popular deep neural network architectures on three image classification benchmarks. Qualitative analysis also indicates that our method helps the networks learn more diverse representations.
In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers. However, a fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages. While this problem has received significant attention recently, most current defenses assume that the workers have identical data. For realistic cases when the data across workers are heterogeneous (non-iid), we design new attacks which circumvent current defenses, leading to significant loss of performance. We then propose a simple bucketing scheme that adapts existing robust algorithms to heterogeneous datasets at a negligible computational cost. We also theoretically and experimentally validate our approach, showing that combining bucketing with existing robust algorithms is effective against challenging attacks. Our work is the first to establish guaranteed convergence for the non-iid Byzantine robust problem under realistic assumptions. 2b1af7f3a8