The document outlines a proposed 8 semester curriculum for a Bachelor's degree in Machine Learning and Data Science. The curriculum covers fundamental topics in mathematics, computer science, statistics, physics and artificial intelligence in the first 4 semesters. Later semesters focus on more advanced topics in artificial intelligence, machine learning, neural networks, databases, and parallel programming. The final semester emphasizes practical applications of machine learning and data science through courses on large-scale systems and non-traditional databases.

1. Ideas for a Machine Learning/Data Science
Bachelor
October 14, 2018
1st Semester
1. Introduction to Modern Algebra
(a) The mathematical way be formal about this.
2. Logic
(a) Concentrate in proof methods and the modeling by logic
3. Discrete Mathematics
(a) Use Donald Knuth Book “Concrete Mathematics”
4. Introduction to Programming
(a) Be precise use the other classes to begin teach algorithms
2nd Semester
1. Differential Calculus
2. Data Structures
(a) Once they have Programming they can understand the basic tools of
programming
3. Object Oriented Programming
4. Probability and Statistics
(a) The Basics on the subject
5. Introduction to Physics
(a) Teach them how to model the world... this is an essential tool
1

2. 3rd Semester
1. Integral Calculus
2. Introduction to Algorithms
(a) Use the Sedgwick to begin to teach them the initial formalisms of
algorithms
3. Human/Machine Interfaces
4. Formal Languages
(a) This is basics for Computer Sciences and Artificial Intelligence
4th Semester
1. Vectorial Calculus
2. Optimization I
(a) Linear Programming mostly
3. Differential Equations
(a) Yes look at some of the basic subjects but teach a little bit of control
4. Algorithms II
(a) He be way bolder go for the Cormen and Dasgupta
5th Semester
1. Artificial Intelligence I
2. Introduction to Databases SQL
(a) We are still using this for obtaining the data
3. Operating Systems
4. Bayesian Methods
(a) Likelihood applications
(b) Maximum A posteriori
(c) Monte Carlo methods
2

3. 6th Semester
1. Intelligent Agents
(a) Once you have Artificial Intelligence you are ready
2. Distributed Operating Systems
3. Introduction to Machine Learning
4. Optimization II
(a) Intro to non-linear programming
(b) Convex Optimization
(c) With you have one of the tools
7th Semester
1. Design of Experiments
(a) You cannot even imagine how good is to have this. It is the Engineer
application of Statistics.
2. Introduction to Neural Networks
(a) The Basics in NN
3. Probabilistic Graphical Models
(a) Bayesian Models
(b) Gibbs Fields
(c) etc
4. Machine Learning and Data Sciences in the World
8th Semester
1. Parallel Programming
2. Large Scale Machine Learning and Data Sciences
(a) We always forget this part in the Education of the Machine Learning
3. Deep Learning
(a) Here the new stuff about deep learning from CNN, Boltzmann Ma-
chines, Auto-encoders, etc
4. Databases Non-SQL
(a) The new wave of data mining
3