Gain insights into machine learning algorithms like regression, deep learning, and SVMs. Learn about Python, probability theory, and optimization through practical coding exercises. Delve into ML intricacies.

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The machine learning field is rapidly advancing today due to the availability of large datasets and the ability to process big data efficiently. Moreover, several new techniques have produced groundbreaking results for standard machine learning problems.

This course provides a detailed description of different machine learning algorithms and techniques, including regression, deep learning, reinforcement learning, Bayes nets, support vector machines (SVMs), and decision trees. The course also offers sufficient mathematical details for a deeper understanding of how different techniques work. An overview of the Python programming language and the fundamental theoretical aspects of ML, including probability theory and optimization, is also included. The course contains several practical coding exercises as well.

By the end of the course, you will have a deep understanding of different machine-learning methods and the ability to choose the right method for different applications.

Mastering Machine Learning Theory and Practice

**(Fill in the blank)** Sentiment analysis is an example of ________.

(Fill in the blank) Sentiment analysis is an example of ________.

**(True or False)** Models with shared weights are, in general, harder to train than  ones with more independent weights.

(True or False) Models with shared weights are, in general, harder to train than ones with more independent weights.

**(Fill in the blank)** The `pad_sequences()` function makes sure that all sequences in a list have the ________ length.

(Fill in the blank) The <code>pad_sequences()</code> function makes sure that all sequences in a list have the ________ length.

**(Fill in the blank)** In $\text{IMDB}$ classification, we added the dropout to the output of the recurrent layer for some regularization to prevent ________.

(Fill in the blank) In <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>IMDB</mtext></mrow><annotation encoding="application/x-tex">\text{IMDB}</annotation></semantics></math>IMDB classification, we added the dropout to the output of the recurrent layer for some regularization to prevent ________.

**(True or False)** In the Hopfield model, each node receives input and can communicate its activity to all other neurons.

(True or False) In the Hopfield model, each node receives input and can communicate its activity to all other neurons.

**(True or False)** The log-likelihood of a model increases the better it approximates the world.

(True or False) The log-likelihood of a model increases the better it approximates the world.

The thermal average of the correlation between two nodes when the states of the visible nodes are fixed.

The thermal average when the recurrent system is running freely.

The thermal average when the recurrent system is not running freely.

The thermal average of the correlation between two nodes when the states of the visible nodes are not fixed.

Which of the following best explains the term labeled as "**clamped**" in the following learning rule:
$$
\Delta w_{i j}=\eta \frac{\partial l}{\partial w_{i j}}=\eta \frac{\beta}{2}\left(\left\langle s_{i} s_{j}\right\rangle_{\text {clamped }}-\left\langle s_{i} s_{j}\right\rangle_{\text {free }}\right)
$$ 

Which of the following best explains the term labeled as “clamped” in the following learning rule:
<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi mathvariant="normal">Δ</mi><msub><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>=</mo><mi>η</mi><mfrac><mrow><mi mathvariant="normal">∂</mi><mi>l</mi></mrow><mrow><mi mathvariant="normal">∂</mi><msub><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub></mrow></mfrac><mo>=</mo><mi>η</mi><mfrac><mi>β</mi><mn>2</mn></mfrac><mrow><mo fence="true">(</mo><msub><mrow><mo fence="true">⟨</mo><msub><mi>s</mi><mi>i</mi></msub><msub><mi>s</mi><mi>j</mi></msub><mo fence="true">⟩</mo></mrow><mtext>clamped </mtext></msub><mo>−</mo><msub><mrow><mo fence="true">⟨</mo><msub><mi>s</mi><mi>i</mi></msub><msub><mi>s</mi><mi>j</mi></msub><mo fence="true">⟩</mo></mrow><mtext>free </mtext></msub><mo fence="true">)</mo></mrow></mrow><annotation encoding="application/x-tex">\Delta w_{i j}=\eta \frac{\partial l}{\partial w_{i j}}=\eta \frac{\beta}{2}\left(\left\langle s_{i} s_{j}\right\rangle_{\text {clamped }}-\left\langle s_{i} s_{j}\right\rangle_{\text {free }}\right)
</annotation></semantics></math>Δwij​=η∂wij​∂l​=η2β​(⟨si​sj​⟩clamped ​−⟨si​sj​⟩free ​)

**(Fill in the blank)**________ architecture has feedback connections.

(Fill in the blank)________ architecture has feedback connections.

Test yourself on cyclic models and recurrent neural networks.

Getting started

Introduction

Scientific Programming with Python

Machine Learning with Sklearn

Neural Networks and Keras

Regression and Optimization

Basic Probability Theory

Probabilistic Regression and Bayes Nets

Generative Models

Cyclic Models and Recurrent Neural Networks

Reinforcement Learning

Artificial intelligence, the Brain, and Our Society

Conclusion

Quiz