Machine Learning Comprehensive Introduction (Giới thiệu về Học máy) - Chittaranjan Hota

UN
Uni24h
ダウンロード数: 0
126
0

資料の内容

1 ページ

Artificial Intelligence (CS F407) Machine Learning: Introduction Chittaranjan Hota Professor, Computer Science & Information Systems Department BITS Pilani Hyderabad Campus, Hyderabad hota@hyderabad.bits-pilani.ac.in

2 ページ

Some tasks… Content Promotion, Content Price Modeling, Adaptive Streaming QoE… ‘cocktail party problem’

3 ページ

What is Learning? • “Learning denotes changes in a system that enables a system to do the same task more efficiently the next time.” – Herbert Simon (Writing your AI Midsem and then Compre…) • Learning is the process of acquiring new or modifying existing knowledge, behaviors, skills, values, or preferences- Wiki • Humans learn from experiences….

4 ページ

What is Machine Learning? x y x1 1 2 … … y System 2 x h h, ,...,h y Input VariablesN 1 2 K M Output Variables Hidden Variables: data data program program output output

5 ページ

Defining the Learning Task Improve on task, T, with respect to performance metric, P, based on experience, E (Tom Mitchell, 1997). T: Playing chess P: Percentage of games won against an arbitrary opponent E: Playing practice games against whom? T: Recognizing hand-written words P: Percentage of words correctly classified E: Database of human-labeled images of handwritten words T: Categorize email messages as spam or legitimate. P: Percentage of email messages correctly classified. E: Database of emails, some with human-given labels

6 ページ

Why Machine Learning is important? • Machine learning is programming computers to optimize a performance criterion using example data. • There is no need to “learn” to calculate payroll • Learning is used when: – Human expertise does not exist • x: Bond graph for a new molecule, f(x): Predicted binding strength to AIDS protease molecule. • Navigating on Mars – Humans are unable to explain their expertise • x: Bitmap picture of hand-written character, f(x): Ascii code of the character • Speech recognition

7 ページ

Continued… – Solution changes in time • x: Description of stock prices and trades for last 20 days, f(x): Recommended stock transactions • Routing on a computer network – Solution needs to be adapted to particular cases • x: Incoming email message, f(x): Importance score for presenting to user (or deleting without presenting) • User biometrics – Relationship and correlations can be hidden within large amounts of data – New knowledge is being constantly discovered by humans

8 ページ

Applications of Machine Learning • natural language processing • search engines • medical diagnosis • detecting credit card fraud • stock market analysis classifying DNA sequences Apache Singa • • speech and handwriting recog. • Spam filtering • Financial Investments (Real estate or Postal deposits) • game playing • adaptive websites • robot locomotion • structural health monitoring • sentiment analysis (Frameworks/Tools)

9 ページ

Few more examples and algos… Rs. 5 lakhs ? lakhs Rs. 80 lakhs Example of Linear Regression… Gradient descent

10 ページ

Few more examples and algos… Boss Cheap Bulk Identifying spam mails Dollar Missing title etc… Naïve Bayes

11 ページ

Few more examples and algos… Recommending Apps to different users … Gender Age App F 17 Instagram F 28 WhatsApp Decision Tree. M 35 FB messenger F 44 WhatsApp M 13 Instagram M 16 Instagram

14 ページ

Issues in Machine Learning • What algorithms are available for learning a concept? How well do they perform? • How much training data is sufficient to learn a concept with high confidence? • Are some training examples more useful than others? • What are the best tasks for a system to learn? • What is the best way for a system to represent it’s knowledge?

15 ページ

ANNs: Nerve cell or neuron Nervous Computational System Abstraction Neuron Node Dendrites Input link and propagation Cell Body Combination function, threshold, activation function Axon Output link Synaptic Connection strength strength/weight Local, • A healthy human brain has around parallel computat 100 billion neurons ion • A neuron may connect to as many as 100,000 other neurons Acknowledgement: Few slides are adopted from R J Mooney’s class slides

最初の15ページを表示しています。全文を見るには資料をダウンロードしてください。

コメント (0)

ログイン して議論に参加する

まだコメントはありません。最初のコメントを書きましょう!