Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

EASL-EORTC clinical practice guidelines : management of hepatocellular carcinoma

Background and Aims:The lack of consensus on how to define and grade adverse postoperative events has greatly hampered the evaluation of surgical procedures. A new classification of complications, initiated in 1992, was updated 5 years ago. It is based on the type of therapy needed to correct the co

The Clavien-Dindo classification of surgical complications: five-year experience.

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Modern Applied Statistics With S

Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

759Background: Microsatellite instability (MSI) has been associated with improved survival outcomes in early stage CRC. In stage IV disease, MSI represents only 3-5% of cases and currently the prog...

Outcomes for metastatic colorectal cancer (mCRC) based on microsatellite instability.

CT-guided fine-needle puncture (FNP) was carried out in 74 patients with pelvic masses and resulted in a correct diagnosis in 36/45 patients (80%) with malignant disease. FNP was true-positive in 66.7% and false-negative in 20%. In benign and inflammatory pelvic space-occupying lesions, FNP confirmed the suspected CT diagnosis in all cases by cytology and bacteriology. CT-guided puncture should always be used complementary to CT when a mass of unclear etiology is present.

CT-guided biopsy of pelvic masses

Hemangiopericytoma is an uncommon vascular tumor with variable malignant potential. The origin, structure and function of pericytes remains controversial. Intra-abdominal hemangiopericytomas are highly aggressive soft tissue tumors with a great propensity for malignant transformation. We report on a case of hemangiopericytoma of the pancreas in a 53-year-old female presenting with abdominal pain. Ultrasonography and CT scan revealed a cystic tumor of the head of the pancreas. The patient underwent successful pancreaticoduodenal resection and is alive with no signs of recurrence 25 months following surgery. Ultrastructural studies are necessary to differentiate hemangiopericytomas from other sarcomas. Malignancy may be ascertained only in the presence of metastases or local recurrence. Routine surveillance is advocated.

Hemangiopericytoma of the pancreas: report of a case and review of the literature

Pancreatic, periampullary and biliary cancer with liver metastases: Should we consider resection in selected cases?

Researcb into tbe regional pain syndrome. Michael Sandow, Peter M. Yellowlees, Guy Maddern, Michael G. Hayes 389 Overuse syndrome in musicians. Richard J. Lederman . 390 Hunter J.H. Fry ..... 390 Rosalie Rebollo Pratt . 390 Tom Hall. . . . . . . . . . .. 390 F.J. Bejjani. ......... 393 Richard N. Norris . . .. 393 Tbe legality of vasectomy. John R. Vallentine 394 P. Gerber . . . . . . . . . . . . . . .. 395 Evidence tbat tbe Australian sylvatic strain of Echinococcus granulosus is infective to bumans. R.C.A. Thompson, D.B. Nott, J. Squire, D. Rennell 396 389

Guy J. Maddern

Papers

Outcomes for metastatic colorectal cancer (mCRC) based on microsatellite instability.

CT-guided biopsy of pelvic masses

Hemangiopericytoma of the pancreas: report of a case and review of the literature

Pancreatic, periampullary and biliary cancer with liver metastases: Should we consider resection in selected cases?

Research into the regional pain syndrome.