Operating System R13 Syllabus

UNIT-I:
Computer System and Operating System Overview: 
Overview of computer operating systems,
operating systems functions,
protection and security,
distributed systems,
special purpose systems,
operating systems structures and
systems calls,
operating systems generation.

UNIT-II:
Process Management
Process concept
process scheduling, operations,
Inter process communication.
Multi Thread programming models.
Process scheduling criteria and algorithms, and their evaluation.

UNIT-III:
Concurrency:
Process synchronization,
the critical- section problem,
Peterson’s Solution,
synchronization Hardware,
semaphores,
classic problems of synchronization,
monitors,
Synchronization examples

UNIT-IV:
Memory Management:
Swapping,
contiguous memory allocation,
paging, structure of the page table,
segmentation
Virtual Memory Management:
virtual memory,
 demand paging,
page-Replacement, algorithms,
Allocation of Frames, Thrashing

UNIT-V:
Principles of deadlock:
system model,
deadlock characterization,
deadlock prevention,
detection and avoidance,
recovery form deadlock,

UNIT-VI:
File system Interface
the concept of a file,
Access Methods,
Directory structure,
File system mounting,
file sharing,
protection.
File System implementation
File system structure,
allocation methods,
free-space management
Mass-storage structure overview of Mass-storage structure,
Disk structure,
disk attachment,
disk scheduling

Comments

Post a Comment

Popular posts from this blog

Tree pruning useful in decision tree induction

Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of tuples to evaluate pruning? The decision tree built may overfit the training data. There could be too many branches, some of which may reflect anomalies in the training data due to noise or outliers. Tree pruning addresses this issue of overfitting the data by removing the least reliable branches (using statistical measures). This generally results in a more compact and reliable decision tree that is faster and more accurate in its classification of data. The drawback of using a separate set of tuples to evaluate pruning is that it may not be representative of the training tuples used to create the original decision tree. If the separate set of tuples are skewed, then using them to evaluate the pruned tree would not be a good indicator of the pruned tree’s classification accuracy. Furthermore, using a separate set of tuples to evaluate pruning means there are less tuples to use for ...
Read more

Machine Learning Syllabus

MACHINE LEARNING UNIT -I: The ingredients of machine learning, Tasks: the problems that can be solved with machine learning, Models: the output of machine learning, Features, the workhorses of machine learning. Binary classification and related tasks: Classification, Scoring and ranking, Class probability estimation UNIT-II: Beyond binary classification:  Handling more than two classes, Regression, Unsupervised and descriptive learning. Concept learning : The hypothesis space, Paths through the hypothesis space, Beyond conjunctive concepts UNIT- III: Tree models: Decision trees, Ranking and probability estimation trees, Tree learning as variance reduction. Rule models: Learning ordered rule lists, Learning unordered rule sets, Descriptive rule learning, First-order rule learning UNIT-IV: Linear models: The least-squares method, The perceptron: a heuristic learning algorithm for linear classifiers, Support vector machines, obtaining probabilities from linear cla...
Read more