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theoiljoint · 5 years

Text

Testimony

{Follows Lady}

Weeks passed.

Trayvon stopped by with a sealed envelope and a smile. “Keep these safe,” he had said quietly, handing over the envelope while some curious patrons stared at the lone human in their midst. “If something happens to them, we can get replacements, but it’s a hassle. Congratulations, Ms. Tonic.”

That night she had stared at the papers he had delivered, reading them over several times but mostly just… staring. She was now, in the eyes of the law, a self-governing automaton. A free ‘bot. A citizen, if only a second-class one.

And she felt… nothing.

I’ve always been my own, she finally thought. Even under Daugherty, even when Jacob was here. This is just an overdue formality.

She wanted to think that her non-reaction was simple impartiality. But she knew that this was just step one of her bid for freedom. She still needed to save the bar, and the idea that her testimony could make or break it was crushing her.

Tipsy didn’t speak to anyone about the coming trial. As much as she was a collector of gossip, she didn’t like rumors about herself getting out, and something like this would definitely get people talking. She didn’t want them to talk about her; she didn’t want them to look down on her for getting in this mess. So when the day for her testimony came, she left Elijah with instructions to turn away any early patrons—they would open when she got back—and she went alone.

The case had been named after the lead plaintiff, Robichaud vs The City of Plifterston. There were so many people testifying in the class action, all with allegations that city hall had taken their “rent” money under the table, that the proceedings had to be broken up across several days. Some had businesses; most were families. And a few, just two besides herself, were automata. But she saw neither of them as Trayvon guided her into the courtroom and to her seat.

“They’re working off a script,” he had said before they entered. “They’ll ask you your name and some personal details. They’ll ask if you’re self-governing. Say ‘yes;’ you don’t have to tell them when you got your papers unless they ask. They’ll ask you when you first started paying the extortion money. Be as precise as you can. Are you alright?”

Tipsy was shaking, wringing her handkerchief between her hands. “I’m scared, Mr. Rider. I could lose everything. Without m-my bar I’m—” The cloth tore slightly, and she bowed her head. Then she felt two hands gently settle on her upper shoulders.

“It’s ok,” Tray said quietly. “You’re helping yourself so much by agreeing to do this. Hornester’s people have to either own up to circumventing the law or deny doing so, and if they deny it, or we win the case, you—all of you—can claim adverse possession. And you have a whole community to back you up on that.”

Tipsy looked up sheepishly. “I’m sorry. I’m being foolish. I’ll do it, I-I’ll—”

“You’re not foolish,” Tray said firmly. “This isn’t easy. And I won’t pretend we’re guaranteed to win. But we’re going to do everything we can, and today, that’s answering their questions.” He paused, and before he could speak again, Tipsy gave a small, soft laugh.

“I’m not the first client you’ve given this pep talk to this week, am I?”

Trayvon grinned. “I’ve asked a hard thing of a great many people, so, maybe a few times.”

“Okay.” Tipsy straightened, and her eyes flashed determinedly. “I’m ready.”

“All rise,” the bailiff said as the side door of the courtroom open. The women who swept in had stately grey hair and a mouth set in a stern thin line. Tipsy struggled to stand up quickly, Trayvon taking her arm to help her balance, and she noticed the slightest glance from the judge in her direction.

Be strong, Tipsy told herself. You’ve stood through worse than this. And she had; she’d put ‘bots three times her size back in their seats. She’d been called every sort of name and ducked her own glassware being thrown back at her. She could handle a few humans looking down their noses at her. She could. She had to.

“Your Honor,” the defending lawyer said, standing and walking to the center of the room. Tipsy thought his name was Nomikos. “I would like to continue where we left off in the list of witnesses.”

The judge nodded.

“I call,” Nomikos began, looking at a sheet of paper in his hand and pausing for the briefest second. “’Ms. Tipsy Tonic’ to the stand,” he said, as if he couldn’t quite believe that was her name.

Tray helped her up again, squeezing her arm for reassurance. He walked her to the stand; there was no ramp up into the box and she had to pull herself up with an undignified little hop. There wasn’t room for her to sit down, not with her unwieldy ‘skirt,’ so she remained standing, folding her hands on the edge of the stand’s wall. A fresh handkerchief was clasped beneath them.

The bailiff lifted a Bible to her, but Tipsy held up a hand, waving it away.

“Please raise your right hand.” She considered for a second, then raised her upper right hand. “Do you affirm to tell the truth, the whole truth, and nothing but the truth?”

“I affirm.”

“What is your name?” Nomikos asked.

“Tipsy Tonic.”

“When were you constructed?”

“1920.” She blinked, then remembering the paperwork Tray had given her, amended, “June 17th.” Her earliest memory came from that day. Being asked to turn on her radio for the ball game.

“Who was your inventor?”

“Orrin Fletcher.”

“How long have you lived in Plifterston?”

“All my life.”

“How long have you lived at 1708 Nantucket Blvd?”

“All my life.”

“Is 1708 your residence or place of business?”

“Both.”

“What business do you do?”

“I run a bar for automatons.”

Nomikos’ lips twitched, but he continued.

“Who is your current engineer?”

“Shi Carlton.”

The lawyer turned to the next stapled page in his hand.

“You too allege that the local government, under direction of Patrick Hornester, has been extorting you for money?”

“Yes.”

“When did this begin?”

“February 11th, 1987.”

“How were you approached?”

“A man named ‘Steward’ came to my b—building that night.”

“What did he say to you?”

“He told me that Jacob—Jacob Begay, my engineer at the time—had reneged on his payments to them. Jake—Jacob—had been living with me. He took care of setting things up, but then he—” A sudden stab of pain went through her. It was an old hurt, but one that still stung. “—He left without telling me, about a month before Steward showed up.”

“What else?”

“Steward laid out the terms of the arrangement—that I would pay a monthly lump sum to city hall, and in doing so avoid taxes and the need to obtain official ownership records.”

“Jacob Begay didn’t leave a deed with you?”

“I… don’t believe he had one,” Tipsy said with a glance at Trayvon. He nodded slightly. “Steward made it sound like he had had the same arrangement with them, but he had never told me about it and I didn’t recognize Steward.”

“How many times did you see Steward?”

“Only three. After that they sent—someone else.”

“Who?”

Tipsy hesitated. Again, Trayvon nodded, slower this time. “A robot named Abacus.”

Someone else sitting at the defendant’s table wrote that down, and Tipsy winced internally. “Can you tell me what you paid that first month?” The lawyer continued.

“$370.”

Nomikos paused. “Do you remember or have records of all the payments you’ve made since then?”

“Yes.”

“Your Honor,” the lawyer said, turning to the judge. “I would like to request the production of evidence relating to this matter.” Trayvon’s brow furrowed and he leaned forward, frowning. Tipsy’s hands tightened.

“How so?” the judge asked. Tipsy realized she hadn’t been listening when her name was announced.

“I would like to obtain the mem.dat from Tipsy Tonic’s processor to validate her story.”

“What!” Trayvon barked, jerking upright. “Objection, Your Honor!”

“Overruled, Mr. Rider,” the judge said dismissively. “Mr. Nomikos, can you provide a justification for such a production?”

“Any and all evidence that is relevant should be considered,” Nomikos said, almost innocently. “And automatons are in an uniquely advantageous position of being able to provide—”

“I won’t do it.”

There was a pause in the courtroom, then a soft buzz of chatter after Tipsy’s statement. Nomikos slowly turned back to look at the ‘bot on the stand, eyeing her pointedly.

“I’m not letting anybody poke around in my processor. Not again.”

“Ms. Tonic,” the judge said, sitting forward and looking down at her. “If the court requests evidence that you possess, you are obligated to produce it.”

“Your Honor, this is not a fair request.” Trayvon stood, trying to keep the heat out of his voice. “Ms. Tonic is under oath, just as every human witness has been. Requiring mem.dat would be a violation—”

“It’s the best proof—” Nomikos started to interrupt him, then Tonic repeated:

“I will not—"

“Order!” the judge snapped, another silence clamping down on the court. “Ms. Tonic, I want to hear why you are so adamantly against providing us this information.”

Trayvon stewed furiously; Tonic could get no reassurance from him. Instead, she looked down, thinking very carefully about how to phrase her response. “I had my skullcap welded in place in 1983,” she said slowly. One thin hand reached up and touched her ‘hair.’ “This cannot be removed without risking destroying parts of my processor. Just having it installed caused some minor but irreparable damage.”

Silence retook the courtroom.

“Why did you have your skullcap welded shut?” Nomikos asked.

“Objection,” Trayvon hissed. “Irrelevant to the proceedings.”

Tipsy looked up at the judge, who had begun to lean back in her chair ponderously. She tapped her fingers on her desk. “Sustained,” she finally said, sitting forward. “I will not force a witness to physically harm themselves to provide evidence. Mr. Rider, you have other automaton witnesses, correct?”

“Yes, Your Honor,” Trayvon said, standing. “And I humbly request that they be excused from similar productions of mem.dat.”

“The issue will be reconsidered at the time they provide testimony,” the judge said. Trayvon grimaced slightly. “However, at this time I do not see a reason to require this particular form of evidence when an alternative can be provided.”

Nomikos was quiet for a moment, then he turned back to Tipsy, who regarded him with an icy stare. “Ms. Tonic, can you provide us with alternate evidence of your payments and interactions?”

“I’ve kept all my ledgers,” Tipsy said coldly. “If those will suffice.”

“Please provide them to Mr. Rider at your earliest convenience. No further questions.”

“Your Honor, may I request a quick recess so that I may speak to my client?” Trayvon asked, standing looking at Tipsy. She was not shaking, but neither was she taking her optics off Nomikos.

“A very short one, Mr. Rider. We will reconvene in ten minutes.”

“I’m so sorry,” Tray said quietly as they exited the courtroom. They pulled into a crevice in the hallway to speak. “I should have expected Nomikos would do something like that. This whole week, and—the other two haven’t testified yet—I wasn’t thinking—”

“Excuse me.”

Tipsy and Trayvon both looked up sharply. A blonde woman had walked up to them, wearing a red suit jacket and holding a pen and pad. She tapped the pen thoughtfully to her chin, just under her red lips, the same shade as her coat. Tipsy had seen her sitting in the back of the courtroom. “My name’s Melissa Etterson. I’m from the Plifterston Cockcrow and I would love to get a statement from you, Ms. Tonic.”

“Hello, Melissa,” Trayvon said through gritted teeth. “I’m sure you would, but if you don’t mind, my client and I are having a private conversation. This is not a good time.”

Melissa’s smile widened, and she effortlessly switched the pen out for a business card. She held this out to Tonic, eyes twinkling. “Well! If you can contact me later, it would be a pleasure to hear more about your story. You can call me anytime. Thank you so much.” Melissa spun on her heel and strode off, back toward the courtroom. Trayvon huffed, glaring after her.

“Don’t call that number,” he said, turning back to Tipsy. “She’s a shark, and she’s brilliant at spinning words.”

“I wasn’t planning to,” Tipsy said, looking around for a trash can. Not seeing one, she dropped the card into her purse for later disposal. “You two act like you have history.”

Trayvon was silent for a moment. “She’s my ex,” he grumbled at last. “Anyway, we were talking, I, uh—”

“You were about to head back in there,” Tipsy said, stopping him. “And make sure your other clients are prepared for their testimonies.” He looked at her strangely, and she nodded. “I don’t want to talk about this, not right now. I need to go home and open my bar; I need work more than anything.”

Tray nodded. “I can get you excused. Don’t leave until I get back, just in case. I just—I’m sorry, Ms.—”

“Go,” she said, and he did.

It was only then that she realized that the handkerchief in her hands was soaked in the oil from her fingers. She lifted one hand to her face and watched the trembling start again. You’ll be home soon, she thought, clenching her fingers into a fist. The hard part is over now.

#ic update #ic #tipsy tonic #trayvon rider #testimony

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zuseeis · 3 years

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#music #spotify

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the-music-master · 3 years

Audio

Inspired by futuristic sounds and sci-fi storylines, High Klassified has blazed one of the most original paths in electronic music, releasing stylish and conceptual EPs on Fool’s Gold while producing platinum hits for Future and The Weeknd. Law Of Automata: Valid is his most ambitious project yet, following HK’s android avatar (as designed by 3D illustrator Serwah Attafuah) as it experiences the full spectrum of human feelings over the course of the EP’s five tracks. These diverse collaborations with R&B singers Leven Kali and Illham, international MCs Zach Zoya and Izi, and EDM beat star TroyBoi each represent a different emotion, and when taken together launch the android - and the listener - to a higher plane of understanding.

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juniperpublishers-robotics-blog · 5 years

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Stable Epistemologies for Thin Clients-Juniper Publishers

Abstract

Many electrical engineers would agree that, had it not been for multicast methodologies, the visualization of multi-processors might never have occurred. In fact, few information theorists would disagree with the development of telephony. We consider how kernels [1] can be applied to the development of RPCs.

Introduction

Unified ubiquitous communication has led to many confusing advances, including rasterization and spread sheets. The notion that theorists agree with DNS is often adamantly opposed. Similarly, a structured quagmire in hard-ware and architecture is the synthesis of telephony. However, multi processors alone will be able to full fill the need for the theoretical unification of sensor networks and spreadsheets.

In order to solve this challenge, we understand how context free grammar can be applied to the emulation of cache coherence. We emphasize that our application stores modular symmetries. We emphasize that our approach allows secure theory. In the opinions of many, we view complexity theory as following a cycle of four phases: location, emulation, emulation, and development. Thus, we see no reason not to use extensible archetypes to synthesize adaptive methodologies.

We question the need for stable archetypes. Unfortunately, this solution is continuously adamantly opposed. Unfortunately, e-business might not be the panacea that statisticians expected. Combined with the construction of Moore's Law, such a claim analyzes a novel application for the improvement of sensor networks.

In our research, we make two main contributions. For starters, we demonstrate not only that red-black trees and agents can collaborate to realize this ambition, but that the same is true for DNS. Further, we disconfirm that I/O automata and e-business can cooperate to surmount this issue.

The roadmap of the paper is as follows. We motivate the need for telephony. Similarly, to answer this quandary, we use modular configurations to validate that von Neumann machines [1,2] and 8.02.11 mesh networks are entirely incompatible. We place our work in context with the related work in this area. On a similar note, we disprove the study of the memory bus. In the end, we conclude.

Related Work

Our solution is related to research into telephony, the location-identity split, and stable archetypes [3]. A comprehensive survey [4] is available in this space. Recent work by Sun et al. [5] suggests a methodology for refining spreadsheets, but does not offer an implementation. The original method to this quandary by Takahashi N was adamantly opposed; on the other hand, this finding did not completely solve this problem. In general, our solution outperformed all related algorithms in this area [6].

The deployment of public-private key pairs has been widely studied. Recent work by Smith et al. [7] suggests a solution for deploying trainable algorithms, but does not offer an implementation. Complexity aside, Still constructs less accurately. Still is broadly related to work in the field of theory by B. Williams et al., but we view it from a new perspective: neural net-works. On a similar note, the foremost algorithm by James Gray does not request the deployment of the location identity split as well as our approach [6-8]. Our method to the study of DHCP differs from that of Smith et al. [9] as well [10]. Therefore, if latency is a concern, our frame work has a clear advantage.

A major source of our inspiration is early work on low energy communication [7,11]. Contrarily, the complexity of their solution grows logarithmically as perfect theory grows. A recent unpublished undergraduate dissertation [12] introduced a similar idea for Bayesian methodologies. Next, J. Suzuki explored several per mutable methods, and reported that they have limited effect on the investigation of scatter/ gather I/O. as a result, despite substantial work in this area, our solution is clearly the method of choice among experts [13-17].

Methodology

In this section, we motivate architecture for improving the producer-consumer problem. De-spite the fact that it might seem perverse, it generally conflicts with the need to provide XML to physicists. The architecture for our heuristic consists of four independent components: ex-pert systems, gigabit switches, game theoretic epistemologies, and web browsers. Though statisticians usually hypothesize the exact opposite, our methodology depends on this property for correct behaviour. Still does not require such an intuitive construction to run correctly, but it doesn't hurt. The question is, will Still satisfy all of these assumptions? Yes [18].

The framework for Still consists of four independent components: courseware, Moore's Law, the investigation of IPv4, and super pages. We show the methodology used by our frame-work in Figure 1. Further, despite the results by Johnson and Qian, we can argue that Markov models and information retrieval systems are largely incompatible. We use our previously visualized results as a basis for all of these assumptions. This is a private property of Still.

Implementation

Though many skeptics said it couldn't be done (most notably Brown and Wu), we construct a fully-working version of our approach. It was necessary to cap the instruction rate used by Still to 774 pages. The server daemon and the hand- optimized compiler must run with the same per-missions [19]. Leading analysts have complete control over the home grown database, which of course is necessary so that massive multiplayer online role-playing games can be made collaborative, read-write, and linear-time. Such a hypothesis at first glance seems counterintuitive but is derived from known results. Further, since our framework cannot be developed to visualize semaphores, hacking the hand-optimized compiler was relatively straightforward. Overall, our heuristic adds only modest overhead and complexity to prior permutable frameworks.

Results

We now discuss our evaluation approach. Our overall evaluation seeks to prove three hypotheses:

I. That the IBM PC Junior of yesteryear actually exhibits better average popularity of voice-over-IP than today's hardware;

II. That the IBM PC Junior of yesteryear actually exhibits better block size than today's hardware; and finally

III. That effective power is a good way to measure effective sampling rate. The reason for this is that studies have shown that mean instruction rate is roughly 42% higher than we might expect [11].

Our logic follows a new model: performance matters only as long as performance constraints take a back seat to effective clock speed [18,19]. Similarly, the reason for this is that studies have shown that bandwidth is roughly 10% higher than we might expect [20]. Our evaluation method will show that doubling the energy of ubiquitous configurations is crucial to our results.

Hardware and software configuration

Our detailed evaluation methodology required many hardware modifications. We carried out a deployment on our 2-node test bed to measure the simplicity of operating systems. Primarily, we removed some 8MHz Intel 386s from our desktop machines. Second, we added 10Gb/s of Ethernet access to MIT's semantic test bed to discover DARPA's planetary-scale cluster [21]. We tripled the distance of Intel's planetary scale overlay network to investigate the effective ROM speed of the NSA's self-learning cluster. Continuing with this rationale, we tripled the sampling rate of our network to investigate the expected bandwidth of Intel's desktop machines. Lastly, we removed more floppy disk space from our encrypted overlay network to better understand our desktop machines.

Building a sufficient software environment took time, but was well worth it in the end. We implemented our scatter/gather I/O server in B, augmented with computationally stochastic extensions. Our experiments soon proved that extreme programming our LISP machines was more effective than auto generating them, as previous work suggested. Second, this concludes our discussion of software modifications.

Experiments and results

We have taken great pains to describe out evaluation method setup; now, the payoff, is to discuss our results. Seizing upon this ideal configuration, we ran four novel experiments:

a. we measured tape drive space as a function of ROM throughput on a Commodore 64;

b. We deployed 72 LISP machines across the sensor-net network, and tested our B-trees accordingly;

c. We compared latency on the Microsoft DOS, DOS and L4 operating systems; and

d. We measured RAM throughput as a function of USB key speed on a Motorola bag telephone.

e. We discarded the results of some earlier experiments, notably when we ran 79 trials with a simulated instant messenger workload, and compared results to our middleware deployment.

We first analyze all four experiments as shown in Figure 1. These median clock speed observations contrast to those seen in earlier work [22], such as X. Nehru's seminal treatise on SMPs and observed effective hard disk space. Continuing with this rationale, the many discontinuities in the graphs point to improved effective interrupt rate introduced with our hardware upgrades. We scarcely anticipated how wildly inaccurate our results were in this phase of the evaluation [23].

We next turn to the first two experiments, shown in Figure 2. It is always a confirmed purpose but has ample historical precedence. The key to Figure 3 is closing the feedback loop; Figure 4 shows how our methodology's effective tape drive throughput does not converge otherwise. The results come from only 1 trial runs, and were not reproducible. Continuing with this rationale, the data in Figure 3, in particular, proves that four years of hard work were wasted on this project [24-26].

Lastly, we discuss the second half of our experiments. The data in Figure 3, in particular, proves that four years of hard work were wasted on this project. Next, note the heavy tail on the CDF in Figure 4, exhibiting weakened signal-to-noise ratio [27]. Note how emulating local-area net-works rather than simulating them in software produce smoother, more reproducible results.

Conclusion

In conclusion, our application will answer many of the challenges faced by today's systems engineers [10]. Our design for analyzing cacheable models is daringly numerous. Therefore, our vision for the future of artificial intelligence certainly includes Still.

For more open acess journals visit our site: juniper publishers

For more articles please click on: Robotics & Automation Engineering Journal

#Juniper Publishers #Open Acess Journal #Robotics #Automation Engineering #Self-ruling robots

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kino97-me · 5 years

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Stanford CS PhD Info

[Disclaimer] All materials are publicly accessible (Jun 2019); compiled based on my personal interest; for learning purpose only.

Graduation requirement

CS300 seminar (autumn only, attend >=2/3)

First-year research rotation: 3 groups, each for 1 quarter

Courses + breadth requirements* by Spring Quarter of year 2

QE by Spring Quarter of year 3

Teaching: 4 units, 1=10h for one quarter

Dissertation/Oral

*Breadth Requirements: 2 subareas within each of the 3 areas

Area A: Mathematical and Theoretical Foundations

A. Analysis of Algorithms:

CS161 Design and Analysis of Algorithms. 3-5 Units.

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or STATS 116.

CS168 The Modern Algorithmic Toolbox. 3-4 Units.

This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS107 and CS161, or permission from the instructor.

CS261 Optimization and Algorithmic Paradigms. 3 Units.

Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to sub-linear algorithms and decision making under uncertainty. Prerequisite: 161 or equivalent.

CS265 Randomized Algorithms and Probabilistic Analysis. 3 Units.

Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.

or CS361 Engineering Design Optimization. 3-4 Units.

Design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles. Prerequisites: some familiarity with probability, programming, and multivariable calculus.

B. Theory of Computation and Complexity Theory:

CS154 Introduction to Automata and Complexity Theory. 3-4 Units.

This course provides a mathematical introduction to the following questions: What is computation? Given a computational model, what problems can we hope to solve in principle with this model? Besides those solvable in principle, what problems can we hope to efficiently solve? In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Not regular? Decidable? Recognizable? Neither? Solvable in P? NP-complete? PSPACE-complete? etc.). Students will gain a deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem. Prerequisites: CS 103 or 103B.

or CS254 Computational Complexity. 3 Units.

An introduction to computational complexity theory. Topics include the P versus NP problem; diagonalization; space complexity: PSPACE, Savitch's theorem, and NL=coNL; counting problems and #P-completeness; circuit complexity; pseudo-randomness and de-randomization; complexity of approximation; quantum computing; complexity barriers. Prerequisites: 154 or equivalent; mathematical maturity.

C. Numerical Analysis and Convex Optimization:

CS 205L Continuous Mathematical Methods with an Emphasis on Machine Learning. 3 Units. (replaces CS205a)

A survey of numerical approaches to the continuous mathematics used in computer vision and robotics with emphasis on machine and deep learning. Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs, etc. (Replaces CS205A, and satisfies all similar requirements.)

CS334a Convex Optimization I. 3 Units. (same as EE364a, or EE364b)

D. Logic:

CS157 Computational Logic. 3 Units.

Rigorous introduction to Symbolic Logic from a computational perspective. Encoding information in the form of logical sentences. Reasoning with information in this form. Overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. Topics include the syntax and semantics of Propositional Logic, Relational Logic, and Herbrand Logic, validity, contingency, unsatisfiability, logical equivalence, entailment, consistency, natural deduction (Fitch), mathematical induction, resolution, compactness, soundness, completeness.

Phil 251 (=Phil 151 Metalogic)

or CS258 Introduction to Programming Language Theory

Area B: Computer Systems

A. Computer Architecture

B. Compilers

C. Networks

D. Programming Languages

E. Software Systems

Area C: Artificial Intelligence and Applications

A. Artificial Intelligence:

CS121 Introduction to Artificial Intelligence. 3 Units.

CS221 Artificial Intelligence: Principles and Techniques. 3-4 Units

OR any TWO of the following:

CS222 Rational Agency and Intelligent Interaction. 3 Units.

For advanced undergraduates, and M.S. and beginning Ph.D. students. Logic-based methods for knowledge representation, information change, and games in artificial intelligence and philosophy. Topics: knowledge, certainty, and belief; time and action; belief dynamics; preference and social choice; games; and desire and intention. Prerequisite: propositional and first-order logic. (Same as PHIL 358)

CS223a Introduction to Robotics. 3 Units.

CS224m Multi-Agent Systems. 3 Units. 2014

CS224N

CS224w Analysis of Networks. 3-4 Units.

CS224U

CS227b General Game Playing. 3 Units.

CS228 Probabilistic Graphical Models: Principles and Techniques. 3-4 Units.

CS229 Machine Learning. 3-4 Units.

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics. (Same as: STATS 229)

CS229t Statistical Learning Theory. 3 Units.

How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. We will present various learning algorithms and prove theoretical guarantees about them. Topics include generalization bounds, implicit regularization, the theory of deep learning, spectral methods, and online learning and bandits problems. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (STATS 315A or CS 229). (Same as STATS 231)

CS231a Computer Vision: From 3D Reconstruction to Recognition. 3-4 Units. (Formerly 223B)

or CS237a Numerical Linear Algebra. 3 Units.

B. Computational Biology

C. Computer Network and Security

D. Databases

E. Graphics

F. HCI

Academic Calendar

Spring quarter: Apr 1 - June 7

Summer quarter: June 25 - August 16

Autumn quarter: September 23 - December 8

Winter quarter: January 7 - March 15

Online Courses

stanford@Youtube

CS230 | Autumn 2018: DL

CS224N | Winter 2019: NLP with DL

CS224U | Spring 2019: NLU

CS234 | Winter 2019: Reinforcement learning

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