# Advance AI notes

> Principles of artificial intelligence. Uninformed and informed search.

Canonical page: [https://chrome-stats.com/d/androidsrc.com.advanceartificialintelligence](https://chrome-stats.com/d/androidsrc.com.advanceartificialintelligence)

## Overview

- **ID:** `androidsrc.com.advanceartificialintelligence`
- **Platform:** Android
- **Type:** Android app
- **Status:** Removed
- **Publisher:** Javed Iqbal
- **Category:** EDUCATION
- **Downloads:** 225
- **Version:** 1.2
- **Last updated:** 2021-12-18
- **First published:** 2021-12-18
- **Data as of:** 2025-02-03
- **Store listing:** [Google Play Store](https://play.google.com/store/apps/details?id=androidsrc.com.advanceartificialintelligence)
- **Website:** [https://www.aibaksolutions.com](https://www.aibaksolutions.com/)
- **Privacy policy:** [https://aibaksolutions.blogspot.com/p/privacy-policy.html](https://aibaksolutions.blogspot.com/p/privacy-policy.html)

## Description

By the end of the semester the students will be able to:<br>understand the search and decision making techniques used in modern artificial intelligence<br>apply artificial intelligence techniques in their own code<br>understand the societal and ethical implications of artificial intelligence<br><br><br>History and positioning of AI<br>	Motivating AI. Dangers of AI and AGI.<br>	Early history<br>	Expert systems<br><br>Uninformed search<br>	Reflex agents<br>	Search problems<br>	Depth first and breadth first search<br>	Uniform cost search<br><br>Informed search: A* search and heuristics<br>	Informed search methods<br>	Heuristics<br>	Greedy search<br>	A* search<br>	Graph search<br><br>Game playing and adversarial search<br>	Types of games<br>	Adversarial search, minimax<br>	The problem of depth<br>	Evaluation functions<br>	Alpha Beta pruning<br><br>Expect Imax search and utilities<br>	Expect Imax search<br>	Refresher about probabilities<br>	Utilities and rationality<br><br>Markov decision processes 1<br>	Defining MDPs: policies and utilities<br>	Optimal policy, value of state, value of Q-state<br><br>Markov decision processes 2<br>	Policy iteration<br><br>Reinforcement learning 1<br>	Reinforcement learning as a twist on MDPs<br><br>Reinforcement learning 2<br>	Exploration vs. exploitation, regret<br>	Generalization across states<br>	Policy search<br><br>Probability<br>	Random variables<br>	Joint and marginal distributions, conditional distribution<br><br>Markov models<br>	Markov chains<br>	Conditional independence<br>	Stationary distributions<br><br>Hidden Markov models<br>	Hidden Markov models<br>	Example: robot localization<br><br>Particle filters and applications of HMMs<br>	Particle filters<br>	Robot localization with particle filters<br>	Dynamic Bayes nets<br><br>Classification, principles of machine learning, naïve Bayes<br>	Classification<br>	Model-based classification<br>	Naive Bayes<br>	Spam filter example<br>	Generalization and overfitting<br>	Parameter estimation<br><br>Introduction to deep learning<br>	History and impact<br><br>Machine learning background of deep learning<br>	History and impact<br>	Machine learning background<br>	Loss functions: squared, cross-entropy, soft max<br>	Optimization, stochastic gradient descent<br>	Backpropagation<br><br>Feedforward neural networks<br>	Feedforward networks<br>	Stochastic gradient descent<br><br>Convolutional neural networks<br>	Convolutions<br>	Convolutional filters in neural networks<br>	Pooling layers

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Source: [Chrome-Stats](https://chrome-stats.com/d/androidsrc.com.advanceartificialintelligence)