Lecture 1: What is Search Engine Optimization?

Lecture 2: How Do Search Engines Work?

Lecture 3: How to Help Spiders and Crawlers

Lecture 4: Domain Name and URL Structure

Lecture 5: Title and Meta Description

Lecture 6: Headings and Images

Lecture 7: Using Keywords in Body Content

Lecture 8: Keyword Research

Lecture 9: Link Building Basics

Lecture 10: How to Build Links

Lecture 11: robots.txt

Lecture 12: Sitemap / sitemap.xml

Lecture 13: Site Analyzer Intro

Lecture 14: Website Crawls

Lecture 15: Analyzing Crawl Results

Lecture 16: Tasks and Creating Reports]]>

Lecture 1: Creating a Socket

Lecture 2: Binding the Socket and Listening for Connections

Lecture 3: Sending Commands to the Client

Lecture 4: Client to Server Connection

Lecture 5: Testing the Program Locally

Lecture 6: Final Program on a Live Server

Lecture 7: Adding Multiple Client Support

Lecture 8: Handling Connections from Multiple Clients

Lecture 9: Creating a Custom Interactive Shell

Lecture 10: Displaying All Current Connections

Lecture 11: Selecting a Target

Lecture 12: Connecting to a Computer Remotely

Lecture 13: Creating the Threads

Lecture 14: Creating an Executable exe File

Lecture 15: Running the Multi-Client Program Live]]>

Lecture 1: Discrete probability distributions (Part 1)

Lecture 2: Discrete probability distributions (Part 2)

Lecture 3: Continuous random variables

Lecture 4: Central Limit Theorem

Lecture 5: Stable distributions

Lecture 6: Stochastic processes

Lecture 7: Markov processes (Part 1)

Lecture 8: Markov processes (Part 2)

Lecture 9: Markov processes (Part 3)

Lecture 10: Birth-and-death processes

Lecture 11: Continous Markov processes

Lecture 12: Langevin dynamics (Part 1)

Lecture 13: Langevin dynamics (Part 2)

Lecture 14: Langevin dynamics (Part 3)

Lecture 15: Langevin dynamics (Part 4)

Lecture 16: Ito^ and Fokker-Planck equations for diffusion processes

Lecture 17: Level-crossing statistics of a continuous random process

Lecture 18: Diffusion of a charged particle in a magnetic field

Lecture 19: Power spectrum of noise

Lecture 20: Elements of linear response theory

Lecture 21: Random pulse sequences

Lecture 22: Dichotomous diffusion

Lecture 23: First passage time (Part 1)

Lecture 24: First passage time (Part 2)

Lecture 25: First passage and recurrence in Markov chains

Lecture 26: Recurrent and transient random walks

Lecture 27: Non-Markovian random walks

Lecture 28: Statistical aspects of deterministic dynamics (Part 1)

Lecture 29: Statistical aspects of deterministic dynamics (Part 2)]]>

Lecture 1: Installing Django

Lecture 2: Creating a Project

Lecture 3: Creating Our First App

Lecture 4: Overview of a Basic App

Lecture 5: Views

Lecture 6: Database Setup

Lecture 7: Creating Models

Lecture 8: Activating Models

Lecture 9: Database API

Lecture 10: Filtering Database Results]]>

Lecture 1: Introduction Micro to Nano A Journey into Intergrated Circuit Technology

Lecture 2: Introduction Micro to Nano A Journey into Intergrated Circuit Technology

Lecture 3: Crystal Properties and Silico Growth

Lecture 4: Crystal Properties and Silico Growth contd.

Lecture 5: IC Fab Labs and Fabrication of IC

Lecture 6: Diffusion

Lecture 7: Diffusion ( cont.)

Lecture 8: Solid State Diffusion

Lecture 9: Solid State Diffusion ( cont.)

Lecture 10: Solid State Diffusion ( cont.)

Lecture 11: Thermal Oxidation of Silicons

Lecture 12: Thermal Oxidation of Silicons

Lecture 13: Thermal Oxidation of Silicons

Lecture 14: Thermal Oxidation of Silicons (cont.)

Lecture 15: Thermal Oxidation of Silicons (cont.)

Lecture 16: Lithography

Lecture 17: Lithography

Lecture 18: Lithography

Lecture 19: ION Implantation

Lecture 20: ION Implantation

Lecture 21: ION Implantation & Silicon IC Processing Flow for CMOS Technology

Lecture 22: ION Implantation & Silicon IC Processing Flow for CMOS Technology

Lecture 23: Silicon IC Processing Flow for CMOS Technology

Lecture 24: Thin Film Deposition

Lecture 25: Thin Film Deposition

Lecture 26: Thin Film Deposition

Lecture 27: Thin Film Deposition & Etching in VLSI Processing

Lecture 28: Etching in VLSI Processing & Back - End Technology]]>

Lecture 1: Creating a New Project

Lecture 2: Queue and Crawled Files

Lecture 3: Adding and Deleting Links

Lecture 4: Speeding Up the Crawler

Lecture 5: Parsing HTML

Lecture 6: Finding Links

Lecture 7: Spider Concept

Lecture 8: Creating the Spider

Lecture 9: Giving the Spider Information

Lecture 10: Booting Up the Spider

Lecture 11: Crawling Pages

Lecture 12: Gathering Links

Lecture 13: Adding Links to the Queue

Lecture 14: Domain Name Parsing

Lecture 15: The First Spider

Lecture 16: Creating Jobs

Lecture 17: Running the Final Program]]>

Lecture 1: Introduction to Ethics - â€˜Critoâ€™ A Socratic dialogue

Lecture 2: Introduction to Ethics -An assessment of Ethical relativism

Lecture 3: Consequentialism -Introduction

Lecture 4: Consequentialism Â Rule & Act

Lecture 5: Hedonism

Lecture 6: Utilitarianism

Lecture 7: Deontological theories Â Introduction

Lecture 8: Deontological theories Â Immanuel Kant

Lecture 9: Ethical Rules (with reference to W D Ross)

Lecture 10: Situation Ethics

Lecture 11: Virtue Ethics

Lecture 12: Metaethical Theories

Lecture 13: Ethical Relativism: A discussion on Universal Declaration of Human Rights

Lecture 14: Ethical Naturalism

Lecture 15: Ethical Naturalism contd

Lecture 16: Ethical Naturalism-Emotivism

Lecture 17: Mod-01 Lec-17a Ethical NonÂnaturalism

Lecture 18: Mod-01 Lec-17b Ethical NonÂnaturalism-II

Lecture 19: NonÂcognitive or Nondescriptivist Theories(Intuitionism)

Lecture 20: NonÂcognitive or Nondescriptivist Theories-Intuitionism Nihilism

Lecture 21: Why be Moral?

Lecture 22: Ethics in the Indian tradition

Lecture 23: Theory of Karma Â Part 1

Lecture 24: Theory of Karma Â Part 2

Lecture 25: Nishkama Karma Â Part 1

Lecture 26: Nishkama Karma Â Part 2

Lecture 27: Gandhian Ethics Â Part 1

Lecture 28: Gandhian Ethics Â Part 2

Lecture 29: Gandhian Ethics Â Part 3 (Satyagraha)

Lecture 30: Purusharthas

Lecture 31: Buddhist Ethics Â Part 1

Lecture 32: Buddhist Ethics Â Part 2-Jaina Ethics

Lecture 33: Some ethical issues (Applied Ethics) â€˜Famine Affluence and Moralityâ€™ Part- I

Lecture 34: Some ethical issues (Applied Ethics) â€˜Famine Affluence and Moralityâ€™ Part- II

Lecture 35: Discussing Thomas Poggeâ€™s â€˜Real World Justiceâ€™ Â Part 1

Lecture 36: Discussing Thomas Poggeâ€™s â€˜Real World Justiceâ€™ Â Part 2

Lecture 37: Discussing Thomas Poggeâ€™s â€˜Real World Justiceâ€™ Â Part 3

Lecture 38: Sexuality: Ethical Perspectives Â Part 1

Lecture 39: Sexuality: Ethical Perspectives Â Part 2]]>

FSM Controller/Datapath and Processor Design:FSM + datapath (GCD example) - FSM + datapath (continued) - Single Cycle MMIPS - Multicycle MMIPS - Multicycle MMIPS – FSM;VLSI Design Automation:Brief Overview of Basic VLSI Design Automation Concepts - Netlist and System Partitioning - Timing Analysis in the context of Physical Design Automation - Placement algorithm;VLSI Design Test and Verification:Introduction to VLSI Testing

VLSI Test Basics:VLSI Testing: Automatic Test Pattern Generation,Design for Test (DFT),Built-In Self-Test (BIST) - VLSI Design Verification: An Introduction Equivalence Checking,Equivalence/Model Checking,Model Checking

Lecture 1: Historical Perspective and Future Trends in CMOS VLSI Circuit and System Design

Lecture 2: Historical Perspective and Future Trends in CMOS VLSI Circuit -Part II

Lecture 3: Logical Effort - A way of Designing Fast CMOS Circuits

Lecture 4: Logical Effort - A way of Designing Fast CMOS Circuits continued

Lecture 5: Logical Effort - A way of Designing Fast CMOS Circuits -Part III

Lecture 6: Power Estimation and Control in CMOS VLSI circuits

Lecture 7: Power Estimation and Control in CMOS VLSI circuits continued

Lecture 8: Low Power Design Techniques

Lecture 9: Low Power Design Techniques -Part II

Lecture 10: Arithmetic Implementation Strategies for VLSI

Lecture 11: Arithmetic Implementation Strategies for VLSI -Part II

Lecture 12: Arithmetic Implementation Strategies for VLSI -Part III

Lecture 13: Arithmetic Implementation Strategies for VLSI -Part IV

Lecture 14: Interconnect aware design: Impact of scaling, buffer insertion and inductive peaking

Lecture 15: Interconnect aware design: Low swing and Current Mod-e signaling

Lecture 16: Interconnect aware design: capacitively coupled interconnects

Lecture 17: Introduction to Hardware Description Languages

Lecture 18: Managing concurrency and time in Hardware Description Languages

Lecture 19: Introduction to VHDL

Lecture 20: Basic Components in VHDL

Lecture 21: Structural Description in VHDL

Lecture 22: Behavioral Description in VHDL

Lecture 23: Introduction to Verilog

Lecture 24: FSM + datapath (GCD example)

Lecture 25: FSM + datapath (continued)

Lecture 26: Single Cycle MMIPS

Lecture 27: Multicycle MMIPS

Lecture 28: Multicycle MMIPS Ã¢ FSM

Lecture 29: Brief Overview of Basic VLSI design Automation Concepts

Lecture 30: Netlist and System Partitioning

Lecture 31: Timing Analysis in the context of Physical design Automation

Lecture 32: Placement algorithm

Lecture 33: Introduction to VLSI Testing

Lecture 34: VLSI Test Basics - I

Lecture 35: VLSI Test Basics - II

Lecture 36: VLSI Testing: Automatic Test Pattern Generation

Lecture 37: VLSI Testing: design for Test (DFT)

Lecture 38: VLSI Testing: Built-in Self-Test (BIST)

Lecture 39: VLSI design Verification: An Introduction

Lecture 40: VLSI design Verification: An Introduction

Lecture 41: VLSI design Verification: Equivalence/Model Checking

Lecture 42: VLSI design Verification: Model Checking]]>

Matrix methods solving LLS:Normal equations – symmetric positive definite (SPD) systems – multiplicative matrix decomposition - Cholesky decomposition- matrix square root - Gramm-Schmidt orthogonalization process - QR decomposition - Singular value decomposition (SVD) - Solution of retrieval problems;Direct minimization methods for solving LLS:LLS as a quadratic minimization problem - Gradient method, its properties - Convergence and speed of convergence of gradient method - Conjugate gradient and Quasi-Newton methods - Practice problems and programming exercises;Deterministic, dynamic models:adjoint method - Dynamic models, role of observations, and least squares objective function, estimation of initial condition (IC) and parameters, adjoint sensitivity - A straight line problem – a warm up - Linear model, first-order adjoint dynamics and computation of the gradient of the least squares objective function - Nonlinear model and first-order adjoint dynamics - Illustrative examples and practice problems, programming exercises

Deterministic, Dynamic models:Other methods - Forward sensitivity method for estimation of IC and parameters, forward sensitivity dynamics - Example of Carbon dynamics,Relation between adjoint and forward sensitivity, Predictability, Lyapunov index - Method of nudging and overview of nudging methods;Static, stochastic models:Bayesian framework - Bayesian method – linear, Gaussian case - Linear minimum variance estimation (LMVE) and prelude to Kalman filter - Model space vs. observation space formulation -Duality between Bayesian and LMVE;Dynamic, Stochastic models:Kalman filtering - Derivation of the Kalman filter equations - Derivation of Nonlinear filter - Computational requirements - Ensemble Kalman filtering;Dynamic stochastic models:Other methods - Unscented Kalman filtering - Particle filtering - An overview and assessment of methods

Lecture 1: An Overview

Lecture 2: Data Mining, Data assimilation and prediction

Lecture 3: A classification of forecast errors

Lecture 4: Finite Dimensional Vector Space

Lecture 5: Matrices

Lecture 6: Matrices Continued

Lecture 7: Multi-variate Calculus

Lecture 8: Optimization in Finite Dimensional Vector spaces

Lecture 9: Deterministic, Static, linear Inverse (well-posed) Problems

Lecture 10: Deterministic, Static, Linear Inverse (Ill-posed) Problems

Lecture 11: A Geometric View - Projections

Lecture 12: Deterministic, Static, nonlinear Inverse Problems

Lecture 13: On-line Least Squares

Lecture 14: Examples of static inverse problems

Lecture 15: Interlude and a Way Forward

Lecture 16: Matrix Decomposition Algorithms

Lecture 17: Matrix Decomposition Algorithms Continued

Lecture 18: Minimization algorithms

Lecture 19: Minimization algorithms Continued

Lecture 20: Inverse problems in deterministic

Lecture 21: Inverse problems in deterministic Continued

Lecture 22: Forward sensitivity method

Lecture 23: Relation between FSM and 4DVAR

Lecture 24: Statistical Estimation

Lecture 25: Statistical Least Squares

Lecture 26: Maximum Likelihood Method

Lecture 27: Bayesian Estimation

Lecture 28: From Gauss to Kalman-Linear Minimum Variance Estimation

Lecture 29: Initialization Classical Method

Lecture 30: Optimal interpolations

Lecture 31: A Bayesian Formation-3D-VAR methods

Lecture 32: Linear Stochastic Dynamics - Kalman Filter

Lecture 33: Linear Stochastic Dynamics - Kalman Filter Continued

Lecture 34: Linear Stochastic Dynamics - Kalman Filter Continued.

Lecture 35: Covariance Square Root Filter

Lecture 36: Nonlinear Filtering

Lecture 37: Ensemble Reduced Rank Filter

Lecture 38: Basic nudging methods

Lecture 39: Deterministic predictability

Lecture 40: Predictability A stochastic view and Summary]]>

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