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• A student knows what machine learning can do and what it can not do. Student knows some topical machine learning solutions.
• A student knows what mathematics is typically required for implementing machine learning solutions and student can implement matrix multiplication
and gradient decent algorithm with Python.
• A student knows what tools are available for machine learning solutions and student can implement simple Python programs using numPy
and Matplotlib Python modules.
• A student understands how neural network is used to identify numbers from gray scale pictures.
• Introduction to machine learning
• Mathematics (matrices, derivative and gradient) for understanding how machines learn
• Programming tools (Python) for implementing machine learning algorithms
• Machine learning “hello world” algorithm for identifying numbers from gray scale pictures with neural network.
0-5. The course contains 4 parts and each part has some material and exercises related to given material. Maximum number of point from the course is 100 points. Course pass criteria and thresholds for different grades will be announced at the beginning of the course (autumn 2019) at course Moodle platform. Introduction (20 points) Mathematics (20 points) Tools/Python (20 points) Machine learning use case (40 points).