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 1 | # The_Math_of_Intelligence
 2 | This is the Syllabus for Siraj Raval's new course "The Math of Intelligence"
 3 | 
 4 | Each week has a short video (released on Friday) and an associated longer video (released on Wednesday). So each weeks short video is in bold and the longer video is underneath.
 5 | 
 6 | ## Week 1 - First order optimization - derivative, partial derivative, convexity
 7 |  SVM Classification with gradient descent
 8 | ## Week 2 - Second order optimization - Jacobian, hessian, laplacian
 9 |  Newtons method for logistic regression
10 | ## Week 3 - Vectors - Vector spaces, vector norms, matrices
11 |  K Means Clustering Algorithm
12 | ## Week 4 - Matrix operations - Dot product, matrix inverse, projections
13 |  Convolutional Neural Network
14 | ## Week 5 - Dimensionality Reduction - matrix decomposition, eigenvectors, eigenvalues
15 |  Recurrent Neural Network
16 | ## Week 6 - Probability Theory - Bayes Theorem, Naive Bayes, Laplace Smoothing 
17 |  Random Forests 
18 |  ## Week 7 - Hyperparameter Optimization - Bayesian vs Frequentist, Distributions, Bayesian Optimization
19 |  Gaussian Mixture Models
20 | ## Week 8 - Stochastic Models - Generative Networks, Latent Dirichlet Allocation, Topic Modeling
21 |  LSTM Networks
22 | ## Week 9 - Reinforcement - Markov chains, Monte Carlo, Markov Decision Processes
23 |  Game Bot 
24 | ## Week 10 - Quantum Machine Learning
25 | 
26 | 


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